3322 lines
73 KiB
C++
3322 lines
73 KiB
C++
/*
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* Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* 3. Neither name of copyright holders nor the names of its contributors
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* may be used to endorse or promote products derived from this software
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* without specific prior written permission.
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*
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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* LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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* NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <ctype.h>
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#include <float.h>
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#include <string.h>
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#include <stdarg.h>
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#include <limits.h>
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#include <locale.h>
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#include <assert.h>
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#include "svm.h"
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int libsvm_version = LIBSVM_VERSION;
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typedef float Qfloat;
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typedef signed char schar;
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#ifndef min
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template <class T> static inline T min(T x,T y) { return (x<y)?x:y; }
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#endif
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#ifndef max
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template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
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#endif
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template <class T> static inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
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template <class S, class T> static inline void clone(T*& dst, S* src, int n)
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{
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dst = new T[n];
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memcpy((void *)dst,(void *)src,sizeof(T)*n);
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}
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static inline double powi(double base, int times)
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{
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double tmp = base, ret = 1.0;
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for(int t=times; t>0; t/=2)
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{
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if(t%2==1) ret*=tmp;
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tmp = tmp * tmp;
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}
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return ret;
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}
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#define INF HUGE_VAL
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#define TAU 1e-12
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#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
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static void print_string_stdout(const char *s)
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{
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fputs(s,stdout);
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fflush(stdout);
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}
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static void (*svm_print_string) (const char *) = &print_string_stdout;
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#if 0
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static void info(const char *fmt,...)
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{
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char buf[BUFSIZ];
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va_list ap;
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va_start(ap,fmt);
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vsprintf(buf,fmt,ap);
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va_end(ap);
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(*svm_print_string)(buf);
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}
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#else
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static void info(const char *fmt,...) {}
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#endif
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//
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// Kernel Cache
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//
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// l is the number of total data items
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// size is the cache size limit in bytes
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//
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class Cache
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{
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public:
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Cache(int l,long int size);
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~Cache();
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// request data [0,len)
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// return some position p where [p,len) need to be filled
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// (p >= len if nothing needs to be filled)
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int get_data(const int index, Qfloat **data, int len);
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void swap_index(int i, int j);
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private:
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int l;
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long int size;
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struct head_t
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{
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head_t *prev, *next; // a circular list
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Qfloat *data;
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int len; // data[0,len) is cached in this entry
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};
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head_t *head;
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head_t lru_head;
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void lru_delete(head_t *h);
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void lru_insert(head_t *h);
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};
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Cache::Cache(int l_,long int size_):l(l_),size(size_)
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{
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head = (head_t *)calloc(l,sizeof(head_t)); // initialized to 0
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size /= sizeof(Qfloat);
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size -= l * sizeof(head_t) / sizeof(Qfloat);
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size = max(size, 2 * (long int) l); // cache must be large enough for two columns
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lru_head.next = lru_head.prev = &lru_head;
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}
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Cache::~Cache()
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{
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for(head_t *h = lru_head.next; h != &lru_head; h=h->next)
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free(h->data);
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free(head);
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}
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void Cache::lru_delete(head_t *h)
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{
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// delete from current location
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h->prev->next = h->next;
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h->next->prev = h->prev;
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}
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void Cache::lru_insert(head_t *h)
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{
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// insert to last position
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h->next = &lru_head;
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h->prev = lru_head.prev;
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h->prev->next = h;
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h->next->prev = h;
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}
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int Cache::get_data(const int index, Qfloat **data, int len)
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{
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head_t *h = &head[index];
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if(h->len) lru_delete(h);
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int more = len - h->len;
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if(more > 0)
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{
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// free old space
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while(size < more)
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{
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head_t *old = lru_head.next;
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lru_delete(old);
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free(old->data);
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size += old->len;
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old->data = 0;
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old->len = 0;
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}
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// allocate new space
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h->data = (Qfloat *)realloc(h->data,sizeof(Qfloat)*len);
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size -= more;
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swap(h->len,len);
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}
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lru_insert(h);
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*data = h->data;
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return len;
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}
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void Cache::swap_index(int i, int j)
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{
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if(i==j) return;
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if(head[i].len) lru_delete(&head[i]);
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if(head[j].len) lru_delete(&head[j]);
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swap(head[i].data,head[j].data);
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swap(head[i].len,head[j].len);
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if(head[i].len) lru_insert(&head[i]);
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if(head[j].len) lru_insert(&head[j]);
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if(i>j) swap(i,j);
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for(head_t *h = lru_head.next; h!=&lru_head; h=h->next)
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{
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if(h->len > i)
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{
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if(h->len > j)
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swap(h->data[i],h->data[j]);
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else
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{
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// give up
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lru_delete(h);
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free(h->data);
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size += h->len;
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h->data = 0;
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h->len = 0;
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}
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}
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}
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}
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//
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// Kernel evaluation
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//
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// the static method k_function is for doing single kernel evaluation
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// the constructor of Kernel prepares to calculate the l*l kernel matrix
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// the member function get_Q is for getting one column from the Q Matrix
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//
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class QMatrix {
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public:
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virtual Qfloat *get_Q(int column, int len) const = 0;
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virtual double *get_QD() const = 0;
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virtual void swap_index(int i, int j) const = 0;
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virtual ~QMatrix() {}
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};
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class Kernel: public QMatrix {
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public:
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Kernel(int l, svm_node * const * x, const svm_parameter& param);
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virtual ~Kernel();
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static double k_function(const svm_node *x, const svm_node *y,
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const svm_parameter& param);
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virtual Qfloat *get_Q(int column, int len) const = 0;
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virtual double *get_QD() const = 0;
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virtual void swap_index(int i, int j) const // no so const...
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{
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swap(x[i],x[j]);
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if(x_square) swap(x_square[i],x_square[j]);
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}
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protected:
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double (Kernel::*kernel_function)(int i, int j) const;
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private:
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const svm_node **x;
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double *x_square;
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// svm_parameter
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const int kernel_type;
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const int degree;
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const double gamma;
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const double coef0;
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static double dot(const svm_node *px, const svm_node *py);
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double kernel_linear(int i, int j) const
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{
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return dot(x[i],x[j]);
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}
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double kernel_poly(int i, int j) const
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{
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return powi(gamma*dot(x[i],x[j])+coef0,degree);
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}
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double kernel_rbf(int i, int j) const
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{
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return exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
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}
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double kernel_sigmoid(int i, int j) const
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{
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return tanh(gamma*dot(x[i],x[j])+coef0);
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}
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double kernel_precomputed(int i, int j) const
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{
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return x[i][(int)(x[j][0].value)].value;
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}
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};
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double k_function(const svm_node* x, const svm_node* y, const svm_parameter& param)
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{
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return Kernel::k_function(x, y, param);
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}
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Kernel::Kernel(int l, svm_node * const * x_, const svm_parameter& param)
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:kernel_type(param.kernel_type), degree(param.degree),
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gamma(param.gamma), coef0(param.coef0)
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{
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switch(kernel_type)
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{
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case LINEAR:
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kernel_function = &Kernel::kernel_linear;
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break;
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case POLY:
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kernel_function = &Kernel::kernel_poly;
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break;
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case RBF:
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kernel_function = &Kernel::kernel_rbf;
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break;
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case SIGMOID:
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kernel_function = &Kernel::kernel_sigmoid;
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break;
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case PRECOMPUTED:
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kernel_function = &Kernel::kernel_precomputed;
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break;
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}
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clone(x,x_,l);
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if(kernel_type == RBF)
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{
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x_square = new double[l];
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for(int i=0;i<l;i++)
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x_square[i] = dot(x[i],x[i]);
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}
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else
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x_square = 0;
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}
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Kernel::~Kernel()
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{
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delete[] x;
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delete[] x_square;
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}
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double Kernel::dot(const svm_node *px, const svm_node *py)
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{
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double sum = 0;
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while(px->index != -1 && py->index != -1)
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{
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if(px->index == py->index)
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{
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sum += px->value * py->value;
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++px;
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++py;
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}
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else
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{
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if(px->index > py->index)
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++py;
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else
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++px;
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}
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}
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return sum;
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}
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double Kernel::k_function(const svm_node *x, const svm_node *y,
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const svm_parameter& param)
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{
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switch(param.kernel_type)
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{
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case LINEAR:
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return dot(x,y);
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case POLY:
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return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
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case RBF:
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{
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double sum = 0;
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while(x->index != -1 && y->index !=-1)
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{
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if(x->index == y->index)
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{
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double d = x->value - y->value;
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sum += d*d;
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++x;
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++y;
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}
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else
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{
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if(x->index > y->index)
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{
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sum += y->value * y->value;
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++y;
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}
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else
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{
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sum += x->value * x->value;
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++x;
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}
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}
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}
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while(x->index != -1)
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{
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sum += x->value * x->value;
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++x;
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}
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while(y->index != -1)
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{
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sum += y->value * y->value;
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++y;
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}
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return exp(-param.gamma*sum);
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}
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case SIGMOID:
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return tanh(param.gamma*dot(x,y)+param.coef0);
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case PRECOMPUTED: //x: test (validation), y: SV
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return x[(int)(y->value)].value;
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default:
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return 0; // Unreachable
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}
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}
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// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
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// Solves:
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//
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// min 0.5(\alpha^T Q \alpha) + p^T \alpha
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//
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// y^T \alpha = \delta
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// y_i = +1 or -1
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// 0 <= alpha_i <= Cp for y_i = 1
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// 0 <= alpha_i <= Cn for y_i = -1
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//
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// Given:
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//
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// Q, p, y, Cp, Cn, and an initial feasible point \alpha
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// l is the size of vectors and matrices
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// eps is the stopping tolerance
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//
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// solution will be put in \alpha, objective value will be put in obj
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//
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class Solver {
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public:
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Solver() {};
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virtual ~Solver() {};
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struct SolutionInfo {
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double obj;
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double rho;
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double *upper_bound;
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double r; // for Solver_NU
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};
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void Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
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double *alpha_, const double* C_, double eps,
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SolutionInfo* si, int shrinking);
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protected:
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int active_size;
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schar *y;
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double *G; // gradient of objective function
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enum { LOWER_BOUND, UPPER_BOUND, FREE };
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char *alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE
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double *alpha;
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const QMatrix *Q;
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const double *QD;
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double eps;
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double Cp,Cn;
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double *C;
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double *p;
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int *active_set;
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double *G_bar; // gradient, if we treat free variables as 0
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int l;
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bool unshrink; // XXX
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double get_C(int i)
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{
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return C[i];
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}
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void update_alpha_status(int i)
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{
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if(alpha[i] >= get_C(i))
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alpha_status[i] = UPPER_BOUND;
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else if(alpha[i] <= 0)
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alpha_status[i] = LOWER_BOUND;
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else alpha_status[i] = FREE;
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}
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bool is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
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bool is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
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bool is_free(int i) { return alpha_status[i] == FREE; }
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void swap_index(int i, int j);
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void reconstruct_gradient();
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virtual int select_working_set(int &i, int &j);
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virtual double calculate_rho();
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virtual void do_shrinking();
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private:
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bool be_shrunk(int i, double Gmax1, double Gmax2);
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};
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void Solver::swap_index(int i, int j)
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{
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Q->swap_index(i,j);
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swap(y[i],y[j]);
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swap(G[i],G[j]);
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swap(alpha_status[i],alpha_status[j]);
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swap(alpha[i],alpha[j]);
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swap(p[i],p[j]);
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swap(active_set[i],active_set[j]);
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swap(G_bar[i],G_bar[j]);
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swap(C[i],C[j]);
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}
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void Solver::reconstruct_gradient()
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{
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// reconstruct inactive elements of G from G_bar and free variables
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if(active_size == l) return;
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int i,j;
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int nr_free = 0;
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for(j=active_size;j<l;j++)
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G[j] = G_bar[j] + p[j];
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for(j=0;j<active_size;j++)
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if(is_free(j))
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nr_free++;
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if(2*nr_free < active_size)
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info("\nWARNING: using -h 0 may be faster\n");
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if (nr_free*l > 2*active_size*(l-active_size))
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{
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for(i=active_size;i<l;i++)
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{
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const Qfloat *Q_i = Q->get_Q(i,active_size);
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for(j=0;j<active_size;j++)
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if(is_free(j))
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G[i] += alpha[j] * Q_i[j];
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}
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}
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else
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{
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for(i=0;i<active_size;i++)
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if(is_free(i))
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{
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const Qfloat *Q_i = Q->get_Q(i,l);
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double alpha_i = alpha[i];
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for(j=active_size;j<l;j++)
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G[j] += alpha_i * Q_i[j];
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}
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}
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}
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void Solver::Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
|
|
double *alpha_, const double* C_, double eps,
|
|
SolutionInfo* si, int shrinking)
|
|
{
|
|
this->l = l;
|
|
this->Q = &Q;
|
|
QD=Q.get_QD();
|
|
clone(p, p_,l);
|
|
clone(y, y_,l);
|
|
clone(alpha,alpha_,l);
|
|
clone(C,C_,l);
|
|
this->eps = eps;
|
|
unshrink = false;
|
|
|
|
// initialize alpha_status
|
|
{
|
|
alpha_status = new char[l];
|
|
for(int i=0;i<l;i++)
|
|
update_alpha_status(i);
|
|
}
|
|
|
|
// initialize active set (for shrinking)
|
|
{
|
|
active_set = new int[l];
|
|
for(int i=0;i<l;i++)
|
|
active_set[i] = i;
|
|
active_size = l;
|
|
}
|
|
|
|
// initialize gradient
|
|
{
|
|
G = new double[l];
|
|
G_bar = new double[l];
|
|
int i;
|
|
for(i=0;i<l;i++)
|
|
{
|
|
G[i] = p[i];
|
|
G_bar[i] = 0;
|
|
}
|
|
for(i=0;i<l;i++)
|
|
if(!is_lower_bound(i))
|
|
{
|
|
const Qfloat *Q_i = Q.get_Q(i,l);
|
|
double alpha_i = alpha[i];
|
|
int j;
|
|
for(j=0;j<l;j++)
|
|
G[j] += alpha_i*Q_i[j];
|
|
if(is_upper_bound(i))
|
|
for(j=0;j<l;j++)
|
|
G_bar[j] += get_C(i) * Q_i[j];
|
|
}
|
|
}
|
|
|
|
// optimization step
|
|
|
|
int iter = 0;
|
|
int max_iter = max(10000000, l>INT_MAX/100 ? INT_MAX : 100*l);
|
|
int counter = min(l,1000)+1;
|
|
|
|
while(iter < max_iter)
|
|
{
|
|
// show progress and do shrinking
|
|
|
|
if(--counter == 0)
|
|
{
|
|
counter = min(l,1000);
|
|
if(shrinking) do_shrinking();
|
|
info(".");
|
|
}
|
|
|
|
int i,j;
|
|
if(select_working_set(i,j)!=0)
|
|
{
|
|
// reconstruct the whole gradient
|
|
reconstruct_gradient();
|
|
// reset active set size and check
|
|
active_size = l;
|
|
info("*");
|
|
if(select_working_set(i,j)!=0)
|
|
break;
|
|
else
|
|
counter = 1; // do shrinking next iteration
|
|
}
|
|
|
|
++iter;
|
|
|
|
// update alpha[i] and alpha[j], handle bounds carefully
|
|
|
|
const Qfloat *Q_i = Q.get_Q(i,active_size);
|
|
const Qfloat *Q_j = Q.get_Q(j,active_size);
|
|
|
|
double C_i = get_C(i);
|
|
double C_j = get_C(j);
|
|
|
|
double old_alpha_i = alpha[i];
|
|
double old_alpha_j = alpha[j];
|
|
|
|
if(y[i]!=y[j])
|
|
{
|
|
double quad_coef = QD[i]+QD[j]+2*Q_i[j];
|
|
if (quad_coef <= 0)
|
|
quad_coef = TAU;
|
|
double delta = (-G[i]-G[j])/quad_coef;
|
|
double diff = alpha[i] - alpha[j];
|
|
alpha[i] += delta;
|
|
alpha[j] += delta;
|
|
|
|
if(diff > 0)
|
|
{
|
|
if(alpha[j] < 0)
|
|
{
|
|
alpha[j] = 0;
|
|
alpha[i] = diff;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(alpha[i] < 0)
|
|
{
|
|
alpha[i] = 0;
|
|
alpha[j] = -diff;
|
|
}
|
|
}
|
|
if(diff > C_i - C_j)
|
|
{
|
|
if(alpha[i] > C_i)
|
|
{
|
|
alpha[i] = C_i;
|
|
alpha[j] = C_i - diff;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(alpha[j] > C_j)
|
|
{
|
|
alpha[j] = C_j;
|
|
alpha[i] = C_j + diff;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
double quad_coef = QD[i]+QD[j]-2*Q_i[j];
|
|
if (quad_coef <= 0)
|
|
quad_coef = TAU;
|
|
double delta = (G[i]-G[j])/quad_coef;
|
|
double sum = alpha[i] + alpha[j];
|
|
alpha[i] -= delta;
|
|
alpha[j] += delta;
|
|
|
|
if(sum > C_i)
|
|
{
|
|
if(alpha[i] > C_i)
|
|
{
|
|
alpha[i] = C_i;
|
|
alpha[j] = sum - C_i;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(alpha[j] < 0)
|
|
{
|
|
alpha[j] = 0;
|
|
alpha[i] = sum;
|
|
}
|
|
}
|
|
if(sum > C_j)
|
|
{
|
|
if(alpha[j] > C_j)
|
|
{
|
|
alpha[j] = C_j;
|
|
alpha[i] = sum - C_j;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(alpha[i] < 0)
|
|
{
|
|
alpha[i] = 0;
|
|
alpha[j] = sum;
|
|
}
|
|
}
|
|
}
|
|
|
|
// update G
|
|
|
|
double delta_alpha_i = alpha[i] - old_alpha_i;
|
|
double delta_alpha_j = alpha[j] - old_alpha_j;
|
|
|
|
for(int k=0;k<active_size;k++)
|
|
{
|
|
G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
|
|
}
|
|
|
|
// update alpha_status and G_bar
|
|
|
|
{
|
|
bool ui = is_upper_bound(i);
|
|
bool uj = is_upper_bound(j);
|
|
update_alpha_status(i);
|
|
update_alpha_status(j);
|
|
int k;
|
|
if(ui != is_upper_bound(i))
|
|
{
|
|
Q_i = Q.get_Q(i,l);
|
|
if(ui)
|
|
for(k=0;k<l;k++)
|
|
G_bar[k] -= C_i * Q_i[k];
|
|
else
|
|
for(k=0;k<l;k++)
|
|
G_bar[k] += C_i * Q_i[k];
|
|
}
|
|
|
|
if(uj != is_upper_bound(j))
|
|
{
|
|
Q_j = Q.get_Q(j,l);
|
|
if(uj)
|
|
for(k=0;k<l;k++)
|
|
G_bar[k] -= C_j * Q_j[k];
|
|
else
|
|
for(k=0;k<l;k++)
|
|
G_bar[k] += C_j * Q_j[k];
|
|
}
|
|
}
|
|
}
|
|
|
|
if(iter >= max_iter)
|
|
{
|
|
if(active_size < l)
|
|
{
|
|
// reconstruct the whole gradient to calculate objective value
|
|
reconstruct_gradient();
|
|
active_size = l;
|
|
info("*");
|
|
}
|
|
fprintf(stderr,"\nWARNING: reaching max number of iterations\n");
|
|
}
|
|
|
|
// calculate rho
|
|
|
|
si->rho = calculate_rho();
|
|
|
|
// calculate objective value
|
|
{
|
|
double v = 0;
|
|
int i;
|
|
for(i=0;i<l;i++)
|
|
v += alpha[i] * (G[i] + p[i]);
|
|
|
|
si->obj = v/2;
|
|
}
|
|
|
|
// put back the solution
|
|
{
|
|
for(int i=0;i<l;i++)
|
|
alpha_[active_set[i]] = alpha[i];
|
|
}
|
|
|
|
// juggle everything back
|
|
/*{
|
|
for(int i=0;i<l;i++)
|
|
while(active_set[i] != i)
|
|
swap_index(i,active_set[i]);
|
|
// or Q.swap_index(i,active_set[i]);
|
|
}*/
|
|
|
|
for(int i=0;i<l;i++)
|
|
si->upper_bound[i] = C[i];
|
|
|
|
info("\noptimization finished, #iter = %d\n",iter);
|
|
|
|
delete[] p;
|
|
delete[] y;
|
|
delete[] C;
|
|
delete[] alpha;
|
|
delete[] alpha_status;
|
|
delete[] active_set;
|
|
delete[] G;
|
|
delete[] G_bar;
|
|
}
|
|
|
|
// return 1 if already optimal, return 0 otherwise
|
|
int Solver::select_working_set(int &out_i, int &out_j)
|
|
{
|
|
// return i,j such that
|
|
// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
|
|
// j: minimizes the decrease of obj value
|
|
// (if quadratic coefficeint <= 0, replace it with tau)
|
|
// -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
|
|
|
|
double Gmax = -INF;
|
|
double Gmax2 = -INF;
|
|
int Gmax_idx = -1;
|
|
int Gmin_idx = -1;
|
|
double obj_diff_min = INF;
|
|
|
|
for(int t=0;t<active_size;t++)
|
|
if(y[t]==+1)
|
|
{
|
|
if(!is_upper_bound(t))
|
|
if(-G[t] >= Gmax)
|
|
{
|
|
Gmax = -G[t];
|
|
Gmax_idx = t;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(!is_lower_bound(t))
|
|
if(G[t] >= Gmax)
|
|
{
|
|
Gmax = G[t];
|
|
Gmax_idx = t;
|
|
}
|
|
}
|
|
|
|
int i = Gmax_idx;
|
|
const Qfloat *Q_i = NULL;
|
|
if(i != -1) // NULL Q_i not accessed: Gmax=-INF if i=-1
|
|
Q_i = Q->get_Q(i,active_size);
|
|
|
|
for(int j=0;j<active_size;j++)
|
|
{
|
|
if(y[j]==+1)
|
|
{
|
|
if (!is_lower_bound(j))
|
|
{
|
|
double grad_diff=Gmax+G[j];
|
|
if (G[j] >= Gmax2)
|
|
Gmax2 = G[j];
|
|
if (grad_diff > 0)
|
|
{
|
|
double obj_diff;
|
|
double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j];
|
|
if (quad_coef > 0)
|
|
obj_diff = -(grad_diff*grad_diff)/quad_coef;
|
|
else
|
|
obj_diff = -(grad_diff*grad_diff)/TAU;
|
|
|
|
if (obj_diff <= obj_diff_min)
|
|
{
|
|
Gmin_idx=j;
|
|
obj_diff_min = obj_diff;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if (!is_upper_bound(j))
|
|
{
|
|
double grad_diff= Gmax-G[j];
|
|
if (-G[j] >= Gmax2)
|
|
Gmax2 = -G[j];
|
|
if (grad_diff > 0)
|
|
{
|
|
double obj_diff;
|
|
double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j];
|
|
if (quad_coef > 0)
|
|
obj_diff = -(grad_diff*grad_diff)/quad_coef;
|
|
else
|
|
obj_diff = -(grad_diff*grad_diff)/TAU;
|
|
|
|
if (obj_diff <= obj_diff_min)
|
|
{
|
|
Gmin_idx=j;
|
|
obj_diff_min = obj_diff;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if(Gmax+Gmax2 < eps)
|
|
return 1;
|
|
|
|
out_i = Gmax_idx;
|
|
out_j = Gmin_idx;
|
|
return 0;
|
|
}
|
|
|
|
bool Solver::be_shrunk(int i, double Gmax1, double Gmax2)
|
|
{
|
|
if(is_upper_bound(i))
|
|
{
|
|
if(y[i]==+1)
|
|
return(-G[i] > Gmax1);
|
|
else
|
|
return(-G[i] > Gmax2);
|
|
}
|
|
else if(is_lower_bound(i))
|
|
{
|
|
if(y[i]==+1)
|
|
return(G[i] > Gmax2);
|
|
else
|
|
return(G[i] > Gmax1);
|
|
}
|
|
else
|
|
return(false);
|
|
}
|
|
|
|
void Solver::do_shrinking()
|
|
{
|
|
int i;
|
|
double Gmax1 = -INF; // max { -y_i * grad(f)_i | i in I_up(\alpha) }
|
|
double Gmax2 = -INF; // max { y_i * grad(f)_i | i in I_low(\alpha) }
|
|
|
|
// find maximal violating pair first
|
|
for(i=0;i<active_size;i++)
|
|
{
|
|
if(y[i]==+1)
|
|
{
|
|
if(!is_upper_bound(i))
|
|
{
|
|
if(-G[i] >= Gmax1)
|
|
Gmax1 = -G[i];
|
|
}
|
|
if(!is_lower_bound(i))
|
|
{
|
|
if(G[i] >= Gmax2)
|
|
Gmax2 = G[i];
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(!is_upper_bound(i))
|
|
{
|
|
if(-G[i] >= Gmax2)
|
|
Gmax2 = -G[i];
|
|
}
|
|
if(!is_lower_bound(i))
|
|
{
|
|
if(G[i] >= Gmax1)
|
|
Gmax1 = G[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
if(unshrink == false && Gmax1 + Gmax2 <= eps*10)
|
|
{
|
|
unshrink = true;
|
|
reconstruct_gradient();
|
|
active_size = l;
|
|
info("*");
|
|
}
|
|
|
|
for(i=0;i<active_size;i++)
|
|
if (be_shrunk(i, Gmax1, Gmax2))
|
|
{
|
|
active_size--;
|
|
while (active_size > i)
|
|
{
|
|
if (!be_shrunk(active_size, Gmax1, Gmax2))
|
|
{
|
|
swap_index(i,active_size);
|
|
break;
|
|
}
|
|
active_size--;
|
|
}
|
|
}
|
|
}
|
|
|
|
double Solver::calculate_rho()
|
|
{
|
|
double r;
|
|
int nr_free = 0;
|
|
double ub = INF, lb = -INF, sum_free = 0;
|
|
for(int i=0;i<active_size;i++)
|
|
{
|
|
double yG = y[i]*G[i];
|
|
|
|
if(is_upper_bound(i))
|
|
{
|
|
if(y[i]==-1)
|
|
ub = min(ub,yG);
|
|
else
|
|
lb = max(lb,yG);
|
|
}
|
|
else if(is_lower_bound(i))
|
|
{
|
|
if(y[i]==+1)
|
|
ub = min(ub,yG);
|
|
else
|
|
lb = max(lb,yG);
|
|
}
|
|
else
|
|
{
|
|
++nr_free;
|
|
sum_free += yG;
|
|
}
|
|
}
|
|
|
|
if(nr_free>0)
|
|
r = sum_free/nr_free;
|
|
else
|
|
r = (ub+lb)/2;
|
|
|
|
return r;
|
|
}
|
|
|
|
//
|
|
// Solver for nu-svm classification and regression
|
|
//
|
|
// additional constraint: e^T \alpha = constant
|
|
//
|
|
class Solver_NU : public Solver
|
|
{
|
|
public:
|
|
Solver_NU() {}
|
|
void Solve(int l, const QMatrix& Q, const double *p, const schar *y,
|
|
double *alpha, double* C_, double eps,
|
|
SolutionInfo* si, int shrinking)
|
|
{
|
|
this->si = si;
|
|
Solver::Solve(l,Q,p,y,alpha,C_,eps,si,shrinking);
|
|
}
|
|
private:
|
|
SolutionInfo *si;
|
|
int select_working_set(int &i, int &j);
|
|
double calculate_rho();
|
|
bool be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4);
|
|
void do_shrinking();
|
|
};
|
|
|
|
// return 1 if already optimal, return 0 otherwise
|
|
int Solver_NU::select_working_set(int &out_i, int &out_j)
|
|
{
|
|
// return i,j such that y_i = y_j and
|
|
// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
|
|
// j: minimizes the decrease of obj value
|
|
// (if quadratic coefficeint <= 0, replace it with tau)
|
|
// -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
|
|
|
|
double Gmaxp = -INF;
|
|
double Gmaxp2 = -INF;
|
|
int Gmaxp_idx = -1;
|
|
|
|
double Gmaxn = -INF;
|
|
double Gmaxn2 = -INF;
|
|
int Gmaxn_idx = -1;
|
|
|
|
int Gmin_idx = -1;
|
|
double obj_diff_min = INF;
|
|
|
|
for(int t=0;t<active_size;t++)
|
|
if(y[t]==+1)
|
|
{
|
|
if(!is_upper_bound(t))
|
|
if(-G[t] >= Gmaxp)
|
|
{
|
|
Gmaxp = -G[t];
|
|
Gmaxp_idx = t;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(!is_lower_bound(t))
|
|
if(G[t] >= Gmaxn)
|
|
{
|
|
Gmaxn = G[t];
|
|
Gmaxn_idx = t;
|
|
}
|
|
}
|
|
|
|
int ip = Gmaxp_idx;
|
|
int in = Gmaxn_idx;
|
|
const Qfloat *Q_ip = NULL;
|
|
const Qfloat *Q_in = NULL;
|
|
if(ip != -1) // NULL Q_ip not accessed: Gmaxp=-INF if ip=-1
|
|
Q_ip = Q->get_Q(ip,active_size);
|
|
if(in != -1)
|
|
Q_in = Q->get_Q(in,active_size);
|
|
|
|
for(int j=0;j<active_size;j++)
|
|
{
|
|
if(y[j]==+1)
|
|
{
|
|
if (!is_lower_bound(j))
|
|
{
|
|
double grad_diff=Gmaxp+G[j];
|
|
if (G[j] >= Gmaxp2)
|
|
Gmaxp2 = G[j];
|
|
if (grad_diff > 0)
|
|
{
|
|
double obj_diff;
|
|
double quad_coef = QD[ip]+QD[j]-2*Q_ip[j];
|
|
if (quad_coef > 0)
|
|
obj_diff = -(grad_diff*grad_diff)/quad_coef;
|
|
else
|
|
obj_diff = -(grad_diff*grad_diff)/TAU;
|
|
|
|
if (obj_diff <= obj_diff_min)
|
|
{
|
|
Gmin_idx=j;
|
|
obj_diff_min = obj_diff;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if (!is_upper_bound(j))
|
|
{
|
|
double grad_diff=Gmaxn-G[j];
|
|
if (-G[j] >= Gmaxn2)
|
|
Gmaxn2 = -G[j];
|
|
if (grad_diff > 0)
|
|
{
|
|
double obj_diff;
|
|
double quad_coef = QD[in]+QD[j]-2*Q_in[j];
|
|
if (quad_coef > 0)
|
|
obj_diff = -(grad_diff*grad_diff)/quad_coef;
|
|
else
|
|
obj_diff = -(grad_diff*grad_diff)/TAU;
|
|
|
|
if (obj_diff <= obj_diff_min)
|
|
{
|
|
Gmin_idx=j;
|
|
obj_diff_min = obj_diff;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if(max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps)
|
|
return 1;
|
|
|
|
if (y[Gmin_idx] == +1)
|
|
out_i = Gmaxp_idx;
|
|
else
|
|
out_i = Gmaxn_idx;
|
|
out_j = Gmin_idx;
|
|
|
|
return 0;
|
|
}
|
|
|
|
bool Solver_NU::be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
|
|
{
|
|
if(is_upper_bound(i))
|
|
{
|
|
if(y[i]==+1)
|
|
return(-G[i] > Gmax1);
|
|
else
|
|
return(-G[i] > Gmax4);
|
|
}
|
|
else if(is_lower_bound(i))
|
|
{
|
|
if(y[i]==+1)
|
|
return(G[i] > Gmax2);
|
|
else
|
|
return(G[i] > Gmax3);
|
|
}
|
|
else
|
|
return(false);
|
|
}
|
|
|
|
void Solver_NU::do_shrinking()
|
|
{
|
|
double Gmax1 = -INF; // max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
|
|
double Gmax2 = -INF; // max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
|
|
double Gmax3 = -INF; // max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
|
|
double Gmax4 = -INF; // max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
|
|
|
|
// find maximal violating pair first
|
|
int i;
|
|
for(i=0;i<active_size;i++)
|
|
{
|
|
if(!is_upper_bound(i))
|
|
{
|
|
if(y[i]==+1)
|
|
{
|
|
if(-G[i] > Gmax1) Gmax1 = -G[i];
|
|
}
|
|
else if(-G[i] > Gmax4) Gmax4 = -G[i];
|
|
}
|
|
if(!is_lower_bound(i))
|
|
{
|
|
if(y[i]==+1)
|
|
{
|
|
if(G[i] > Gmax2) Gmax2 = G[i];
|
|
}
|
|
else if(G[i] > Gmax3) Gmax3 = G[i];
|
|
}
|
|
}
|
|
|
|
if(unshrink == false && max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10)
|
|
{
|
|
unshrink = true;
|
|
reconstruct_gradient();
|
|
active_size = l;
|
|
}
|
|
|
|
for(i=0;i<active_size;i++)
|
|
if (be_shrunk(i, Gmax1, Gmax2, Gmax3, Gmax4))
|
|
{
|
|
active_size--;
|
|
while (active_size > i)
|
|
{
|
|
if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
|
|
{
|
|
swap_index(i,active_size);
|
|
break;
|
|
}
|
|
active_size--;
|
|
}
|
|
}
|
|
}
|
|
|
|
double Solver_NU::calculate_rho()
|
|
{
|
|
int nr_free1 = 0,nr_free2 = 0;
|
|
double ub1 = INF, ub2 = INF;
|
|
double lb1 = -INF, lb2 = -INF;
|
|
double sum_free1 = 0, sum_free2 = 0;
|
|
|
|
for(int i=0;i<active_size;i++)
|
|
{
|
|
if(y[i]==+1)
|
|
{
|
|
if(is_upper_bound(i))
|
|
lb1 = max(lb1,G[i]);
|
|
else if(is_lower_bound(i))
|
|
ub1 = min(ub1,G[i]);
|
|
else
|
|
{
|
|
++nr_free1;
|
|
sum_free1 += G[i];
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(is_upper_bound(i))
|
|
lb2 = max(lb2,G[i]);
|
|
else if(is_lower_bound(i))
|
|
ub2 = min(ub2,G[i]);
|
|
else
|
|
{
|
|
++nr_free2;
|
|
sum_free2 += G[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
double r1,r2;
|
|
if(nr_free1 > 0)
|
|
r1 = sum_free1/nr_free1;
|
|
else
|
|
r1 = (ub1+lb1)/2;
|
|
|
|
if(nr_free2 > 0)
|
|
r2 = sum_free2/nr_free2;
|
|
else
|
|
r2 = (ub2+lb2)/2;
|
|
|
|
si->r = (r1+r2)/2;
|
|
return (r1-r2)/2;
|
|
}
|
|
|
|
//
|
|
// Q matrices for various formulations
|
|
//
|
|
class SVC_Q: public Kernel
|
|
{
|
|
public:
|
|
SVC_Q(const svm_problem& prob, const svm_parameter& param, const schar *y_)
|
|
:Kernel(prob.l, prob.x, param)
|
|
{
|
|
clone(y,y_,prob.l);
|
|
cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
|
|
QD = new double[prob.l];
|
|
for(int i=0;i<prob.l;i++)
|
|
QD[i] = (this->*kernel_function)(i,i);
|
|
}
|
|
|
|
Qfloat *get_Q(int i, int len) const
|
|
{
|
|
Qfloat *data;
|
|
int start, j;
|
|
if((start = cache->get_data(i,&data,len)) < len)
|
|
{
|
|
for(j=start;j<len;j++)
|
|
data[j] = (Qfloat)(y[i]*y[j]*(this->*kernel_function)(i,j));
|
|
}
|
|
return data;
|
|
}
|
|
|
|
double *get_QD() const
|
|
{
|
|
return QD;
|
|
}
|
|
|
|
void swap_index(int i, int j) const
|
|
{
|
|
cache->swap_index(i,j);
|
|
Kernel::swap_index(i,j);
|
|
swap(y[i],y[j]);
|
|
swap(QD[i],QD[j]);
|
|
}
|
|
|
|
~SVC_Q()
|
|
{
|
|
delete[] y;
|
|
delete cache;
|
|
delete[] QD;
|
|
}
|
|
private:
|
|
schar *y;
|
|
Cache *cache;
|
|
double *QD;
|
|
};
|
|
|
|
class ONE_CLASS_Q: public Kernel
|
|
{
|
|
public:
|
|
ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param)
|
|
:Kernel(prob.l, prob.x, param)
|
|
{
|
|
cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
|
|
QD = new double[prob.l];
|
|
for(int i=0;i<prob.l;i++)
|
|
QD[i] = (this->*kernel_function)(i,i);
|
|
}
|
|
|
|
Qfloat *get_Q(int i, int len) const
|
|
{
|
|
Qfloat *data;
|
|
int start, j;
|
|
if((start = cache->get_data(i,&data,len)) < len)
|
|
{
|
|
for(j=start;j<len;j++)
|
|
data[j] = (Qfloat)(this->*kernel_function)(i,j);
|
|
}
|
|
return data;
|
|
}
|
|
|
|
double *get_QD() const
|
|
{
|
|
return QD;
|
|
}
|
|
|
|
void swap_index(int i, int j) const
|
|
{
|
|
cache->swap_index(i,j);
|
|
Kernel::swap_index(i,j);
|
|
swap(QD[i],QD[j]);
|
|
}
|
|
|
|
~ONE_CLASS_Q()
|
|
{
|
|
delete cache;
|
|
delete[] QD;
|
|
}
|
|
private:
|
|
Cache *cache;
|
|
double *QD;
|
|
};
|
|
|
|
class SVR_Q: public Kernel
|
|
{
|
|
public:
|
|
SVR_Q(const svm_problem& prob, const svm_parameter& param)
|
|
:Kernel(prob.l, prob.x, param)
|
|
{
|
|
l = prob.l;
|
|
cache = new Cache(l,(long int)(param.cache_size*(1<<20)));
|
|
QD = new double[2*l];
|
|
sign = new schar[2*l];
|
|
index = new int[2*l];
|
|
for(int k=0;k<l;k++)
|
|
{
|
|
sign[k] = 1;
|
|
sign[k+l] = -1;
|
|
index[k] = k;
|
|
index[k+l] = k;
|
|
QD[k] = (this->*kernel_function)(k,k);
|
|
QD[k+l] = QD[k];
|
|
}
|
|
buffer[0] = new Qfloat[2*l];
|
|
buffer[1] = new Qfloat[2*l];
|
|
next_buffer = 0;
|
|
}
|
|
|
|
void swap_index(int i, int j) const
|
|
{
|
|
swap(sign[i],sign[j]);
|
|
swap(index[i],index[j]);
|
|
swap(QD[i],QD[j]);
|
|
}
|
|
|
|
Qfloat *get_Q(int i, int len) const
|
|
{
|
|
Qfloat *data;
|
|
int j, real_i = index[i];
|
|
if(cache->get_data(real_i,&data,l) < l)
|
|
{
|
|
for(j=0;j<l;j++)
|
|
data[j] = (Qfloat)(this->*kernel_function)(real_i,j);
|
|
}
|
|
|
|
// reorder and copy
|
|
Qfloat *buf = buffer[next_buffer];
|
|
next_buffer = 1 - next_buffer;
|
|
schar si = sign[i];
|
|
for(j=0;j<len;j++)
|
|
buf[j] = (Qfloat) si * (Qfloat) sign[j] * data[index[j]];
|
|
return buf;
|
|
}
|
|
|
|
double *get_QD() const
|
|
{
|
|
return QD;
|
|
}
|
|
|
|
~SVR_Q()
|
|
{
|
|
delete cache;
|
|
delete[] sign;
|
|
delete[] index;
|
|
delete[] buffer[0];
|
|
delete[] buffer[1];
|
|
delete[] QD;
|
|
}
|
|
private:
|
|
int l;
|
|
Cache *cache;
|
|
schar *sign;
|
|
int *index;
|
|
mutable int next_buffer;
|
|
Qfloat *buffer[2];
|
|
double *QD;
|
|
};
|
|
#include <iostream>
|
|
//
|
|
// construct and solve various formulations
|
|
//
|
|
static void solve_c_svc(
|
|
const svm_problem *prob, const svm_parameter* param,
|
|
double *alpha, Solver::SolutionInfo* si, double Cp, double Cn)
|
|
{
|
|
int l = prob->l;
|
|
double *minus_ones = new double[l];
|
|
schar *y = new schar[l];
|
|
double *C = new double[l];
|
|
|
|
int i;
|
|
|
|
for(i=0;i<l;i++)
|
|
{
|
|
alpha[i] = 0;
|
|
minus_ones[i] = -1;
|
|
if(prob->y[i] > 0)
|
|
{
|
|
y[i] = +1;
|
|
C[i] = prob->W[i]*Cp;
|
|
}
|
|
else
|
|
{
|
|
y[i] = -1;
|
|
C[i] = prob->W[i]*Cn;
|
|
}
|
|
//std::cout << C[i] << " ";
|
|
}
|
|
//std::cout << std::endl;
|
|
|
|
Solver s;
|
|
s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
|
|
alpha, C, param->eps, si, param->shrinking);
|
|
|
|
/*
|
|
double sum_alpha=0;
|
|
for(i=0;i<l;i++)
|
|
sum_alpha += alpha[i];
|
|
if (Cp==Cn)
|
|
info("nu = %f\n", sum_alpha/(Cp*prob->l));
|
|
*/
|
|
|
|
for(i=0;i<l;i++)
|
|
alpha[i] *= y[i];
|
|
|
|
delete[] C;
|
|
delete[] minus_ones;
|
|
delete[] y;
|
|
}
|
|
|
|
static void solve_nu_svc(
|
|
const svm_problem *prob, const svm_parameter *param,
|
|
double *alpha, Solver::SolutionInfo* si)
|
|
{
|
|
int i;
|
|
int l = prob->l;
|
|
double nu = param->nu;
|
|
|
|
schar *y = new schar[l];
|
|
double *C = new double[l];
|
|
|
|
for(i=0;i<l;i++)
|
|
{
|
|
if(prob->y[i]>0)
|
|
y[i] = +1;
|
|
else
|
|
y[i] = -1;
|
|
C[i] = prob->W[i];
|
|
}
|
|
|
|
double nu_l = 0;
|
|
for(i=0;i<l;i++) nu_l += nu*C[i];
|
|
double sum_pos = nu_l/2;
|
|
double sum_neg = nu_l/2;
|
|
|
|
for(i=0;i<l;i++)
|
|
if(y[i] == +1)
|
|
{
|
|
alpha[i] = min(C[i],sum_pos);
|
|
sum_pos -= alpha[i];
|
|
}
|
|
else
|
|
{
|
|
alpha[i] = min(C[i],sum_neg);
|
|
sum_neg -= alpha[i];
|
|
}
|
|
|
|
double *zeros = new double[l];
|
|
|
|
for(i=0;i<l;i++)
|
|
zeros[i] = 0;
|
|
|
|
Solver_NU s;
|
|
s.Solve(l, SVC_Q(*prob,*param,y), zeros, y,
|
|
alpha, C, param->eps, si, param->shrinking);
|
|
double r = si->r;
|
|
|
|
info("C = %f\n",1/r);
|
|
|
|
for(i=0;i<l;i++)
|
|
{
|
|
alpha[i] *= y[i]/r;
|
|
si->upper_bound[i] /= r;
|
|
}
|
|
|
|
si->rho /= r;
|
|
si->obj /= (r*r);
|
|
|
|
delete[] C;
|
|
delete[] y;
|
|
delete[] zeros;
|
|
}
|
|
|
|
static void solve_one_class(
|
|
const svm_problem *prob, const svm_parameter *param,
|
|
double *alpha, Solver::SolutionInfo* si)
|
|
{
|
|
int l = prob->l;
|
|
double *zeros = new double[l];
|
|
schar *ones = new schar[l];
|
|
double *C = new double[l];
|
|
int i;
|
|
|
|
double nu_l = 0;
|
|
|
|
for(i=0;i<l;i++)
|
|
{
|
|
C[i] = prob->W[i];
|
|
nu_l += C[i] * param->nu;
|
|
}
|
|
|
|
i = 0;
|
|
while(nu_l > 0)
|
|
{
|
|
alpha[i] = min(C[i],nu_l);
|
|
nu_l -= alpha[i];
|
|
++i;
|
|
}
|
|
for(;i<l;i++)
|
|
alpha[i] = 0;
|
|
|
|
for(i=0;i<l;i++)
|
|
{
|
|
zeros[i] = 0;
|
|
ones[i] = 1;
|
|
}
|
|
|
|
Solver s;
|
|
s.Solve(l, ONE_CLASS_Q(*prob,*param), zeros, ones,
|
|
alpha, C, param->eps, si, param->shrinking);
|
|
|
|
delete[] C;
|
|
delete[] zeros;
|
|
delete[] ones;
|
|
}
|
|
|
|
static void solve_epsilon_svr(
|
|
const svm_problem *prob, const svm_parameter *param,
|
|
double *alpha, Solver::SolutionInfo* si)
|
|
{
|
|
int l = prob->l;
|
|
double *alpha2 = new double[2*l];
|
|
double *linear_term = new double[2*l];
|
|
double *C = new double[2*l];
|
|
schar *y = new schar[2*l];
|
|
int i;
|
|
|
|
for(i=0;i<l;i++)
|
|
{
|
|
alpha2[i] = 0;
|
|
linear_term[i] = param->p - prob->y[i];
|
|
y[i] = 1;
|
|
C[i] = prob->W[i]*param->C;
|
|
|
|
alpha2[i+l] = 0;
|
|
linear_term[i+l] = param->p + prob->y[i];
|
|
y[i+l] = -1;
|
|
C[i+l] = prob->W[i]*param->C;
|
|
}
|
|
|
|
Solver s;
|
|
s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y,
|
|
alpha2, C, param->eps, si, param->shrinking);
|
|
double sum_alpha = 0;
|
|
for(i=0;i<l;i++)
|
|
{
|
|
alpha[i] = alpha2[i] - alpha2[i+l];
|
|
sum_alpha += fabs(alpha[i]);
|
|
}
|
|
//info("nu = %f\n",sum_alpha/(param->C*l));
|
|
delete[] alpha2;
|
|
delete[] linear_term;
|
|
delete[] C;
|
|
delete[] y;
|
|
}
|
|
|
|
static void solve_nu_svr(
|
|
const svm_problem *prob, const svm_parameter *param,
|
|
double *alpha, Solver::SolutionInfo* si)
|
|
{
|
|
int l = prob->l;
|
|
double *C = new double[2*l];
|
|
double *alpha2 = new double[2*l];
|
|
double *linear_term = new double[2*l];
|
|
schar *y = new schar[2*l];
|
|
int i;
|
|
|
|
double sum = 0;
|
|
for(i=0;i<l;i++)
|
|
{
|
|
C[i] = C[i+l] = prob->W[i]*param->C;
|
|
sum += C[i] * param->nu;
|
|
}
|
|
sum /= 2;
|
|
|
|
for(i=0;i<l;i++)
|
|
{
|
|
alpha2[i] = alpha2[i+l] = min(sum,C[i]);
|
|
sum -= alpha2[i];
|
|
|
|
linear_term[i] = - prob->y[i];
|
|
y[i] = 1;
|
|
|
|
linear_term[i+l] = prob->y[i];
|
|
y[i+l] = -1;
|
|
}
|
|
|
|
Solver_NU s;
|
|
s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y,
|
|
alpha2, C, param->eps, si, param->shrinking);
|
|
|
|
info("epsilon = %f\n",-si->r);
|
|
|
|
for(i=0;i<l;i++)
|
|
alpha[i] = alpha2[i] - alpha2[i+l];
|
|
|
|
delete[] alpha2;
|
|
delete[] linear_term;
|
|
delete[] C;
|
|
delete[] y;
|
|
}
|
|
|
|
//
|
|
// decision_function
|
|
//
|
|
struct decision_function
|
|
{
|
|
double *alpha;
|
|
double rho;
|
|
};
|
|
|
|
static decision_function svm_train_one(
|
|
const svm_problem *prob, const svm_parameter *param,
|
|
double Cp, double Cn)
|
|
{
|
|
double *alpha = Malloc(double,prob->l);
|
|
Solver::SolutionInfo si;
|
|
switch(param->svm_type)
|
|
{
|
|
case C_SVC:
|
|
si.upper_bound = Malloc(double,prob->l);
|
|
solve_c_svc(prob,param,alpha,&si,Cp,Cn);
|
|
break;
|
|
case NU_SVC:
|
|
si.upper_bound = Malloc(double,prob->l);
|
|
solve_nu_svc(prob,param,alpha,&si);
|
|
break;
|
|
case ONE_CLASS:
|
|
si.upper_bound = Malloc(double,prob->l);
|
|
solve_one_class(prob,param,alpha,&si);
|
|
break;
|
|
case EPSILON_SVR:
|
|
si.upper_bound = Malloc(double,2*prob->l);
|
|
solve_epsilon_svr(prob,param,alpha,&si);
|
|
break;
|
|
case NU_SVR:
|
|
si.upper_bound = Malloc(double,2*prob->l);
|
|
solve_nu_svr(prob,param,alpha,&si);
|
|
break;
|
|
}
|
|
|
|
info("obj = %f, rho = %f\n",si.obj,si.rho);
|
|
|
|
// output SVs
|
|
|
|
int nSV = 0;
|
|
int nBSV = 0;
|
|
for(int i=0;i<prob->l;i++)
|
|
{
|
|
if(fabs(alpha[i]) > 0)
|
|
{
|
|
++nSV;
|
|
if(prob->y[i] > 0)
|
|
{
|
|
if(fabs(alpha[i]) >= si.upper_bound[i])
|
|
++nBSV;
|
|
}
|
|
else
|
|
{
|
|
if(fabs(alpha[i]) >= si.upper_bound[i])
|
|
++nBSV;
|
|
}
|
|
}
|
|
}
|
|
|
|
free(si.upper_bound);
|
|
|
|
info("nSV = %d, nBSV = %d\n",nSV,nBSV);
|
|
|
|
decision_function f;
|
|
f.alpha = alpha;
|
|
f.rho = si.rho;
|
|
return f;
|
|
}
|
|
|
|
// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
|
|
static void sigmoid_train(
|
|
int l, const double *dec_values, const double *labels,
|
|
double& A, double& B)
|
|
{
|
|
double prior1=0, prior0 = 0;
|
|
int i;
|
|
|
|
for (i=0;i<l;i++)
|
|
if (labels[i] > 0) prior1+=1;
|
|
else prior0+=1;
|
|
|
|
int max_iter=100; // Maximal number of iterations
|
|
double min_step=1e-10; // Minimal step taken in line search
|
|
double sigma=1e-12; // For numerically strict PD of Hessian
|
|
double eps=1e-5;
|
|
double hiTarget=(prior1+1.0)/(prior1+2.0);
|
|
double loTarget=1/(prior0+2.0);
|
|
double *t=Malloc(double,l);
|
|
double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
|
|
double newA,newB,newf,d1,d2;
|
|
int iter;
|
|
|
|
// Initial Point and Initial Fun Value
|
|
A=0.0; B=log((prior0+1.0)/(prior1+1.0));
|
|
double fval = 0.0;
|
|
|
|
for (i=0;i<l;i++)
|
|
{
|
|
if (labels[i]>0) t[i]=hiTarget;
|
|
else t[i]=loTarget;
|
|
fApB = dec_values[i]*A+B;
|
|
if (fApB>=0)
|
|
fval += t[i]*fApB + log(1+exp(-fApB));
|
|
else
|
|
fval += (t[i] - 1)*fApB +log(1+exp(fApB));
|
|
}
|
|
for (iter=0;iter<max_iter;iter++)
|
|
{
|
|
// Update Gradient and Hessian (use H' = H + sigma I)
|
|
h11=sigma; // numerically ensures strict PD
|
|
h22=sigma;
|
|
h21=0.0;g1=0.0;g2=0.0;
|
|
for (i=0;i<l;i++)
|
|
{
|
|
fApB = dec_values[i]*A+B;
|
|
if (fApB >= 0)
|
|
{
|
|
p=exp(-fApB)/(1.0+exp(-fApB));
|
|
q=1.0/(1.0+exp(-fApB));
|
|
}
|
|
else
|
|
{
|
|
p=1.0/(1.0+exp(fApB));
|
|
q=exp(fApB)/(1.0+exp(fApB));
|
|
}
|
|
d2=p*q;
|
|
h11+=dec_values[i]*dec_values[i]*d2;
|
|
h22+=d2;
|
|
h21+=dec_values[i]*d2;
|
|
d1=t[i]-p;
|
|
g1+=dec_values[i]*d1;
|
|
g2+=d1;
|
|
}
|
|
|
|
// Stopping Criteria
|
|
if (fabs(g1)<eps && fabs(g2)<eps)
|
|
break;
|
|
|
|
// Finding Newton direction: -inv(H') * g
|
|
det=h11*h22-h21*h21;
|
|
dA=-(h22*g1 - h21 * g2) / det;
|
|
dB=-(-h21*g1+ h11 * g2) / det;
|
|
gd=g1*dA+g2*dB;
|
|
|
|
|
|
stepsize = 1; // Line Search
|
|
while (stepsize >= min_step)
|
|
{
|
|
newA = A + stepsize * dA;
|
|
newB = B + stepsize * dB;
|
|
|
|
// New function value
|
|
newf = 0.0;
|
|
for (i=0;i<l;i++)
|
|
{
|
|
fApB = dec_values[i]*newA+newB;
|
|
if (fApB >= 0)
|
|
newf += t[i]*fApB + log(1+exp(-fApB));
|
|
else
|
|
newf += (t[i] - 1)*fApB +log(1+exp(fApB));
|
|
}
|
|
// Check sufficient decrease
|
|
if (newf<fval+0.0001*stepsize*gd)
|
|
{
|
|
A=newA;B=newB;fval=newf;
|
|
break;
|
|
}
|
|
else
|
|
stepsize = stepsize / 2.0;
|
|
}
|
|
|
|
if (stepsize < min_step)
|
|
{
|
|
info("Line search fails in two-class probability estimates\n");
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (iter>=max_iter)
|
|
info("Reaching maximal iterations in two-class probability estimates\n");
|
|
free(t);
|
|
}
|
|
|
|
static double sigmoid_predict(double decision_value, double A, double B)
|
|
{
|
|
double fApB = decision_value*A+B;
|
|
// 1-p used later; avoid catastrophic cancellation
|
|
if (fApB >= 0)
|
|
return exp(-fApB)/(1.0+exp(-fApB));
|
|
else
|
|
return 1.0/(1+exp(fApB)) ;
|
|
}
|
|
|
|
// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
|
|
static void multiclass_probability(int k, double **r, double *p)
|
|
{
|
|
int t,j;
|
|
int iter = 0, max_iter=max(100,k);
|
|
double **Q=Malloc(double *,k);
|
|
double *Qp=Malloc(double,k);
|
|
double pQp, eps=0.005/k;
|
|
|
|
for (t=0;t<k;t++)
|
|
{
|
|
p[t]=1.0/k; // Valid if k = 1
|
|
Q[t]=Malloc(double,k);
|
|
Q[t][t]=0;
|
|
for (j=0;j<t;j++)
|
|
{
|
|
Q[t][t]+=r[j][t]*r[j][t];
|
|
Q[t][j]=Q[j][t];
|
|
}
|
|
for (j=t+1;j<k;j++)
|
|
{
|
|
Q[t][t]+=r[j][t]*r[j][t];
|
|
Q[t][j]=-r[j][t]*r[t][j];
|
|
}
|
|
}
|
|
for (iter=0;iter<max_iter;iter++)
|
|
{
|
|
// stopping condition, recalculate QP,pQP for numerical accuracy
|
|
pQp=0;
|
|
for (t=0;t<k;t++)
|
|
{
|
|
Qp[t]=0;
|
|
for (j=0;j<k;j++)
|
|
Qp[t]+=Q[t][j]*p[j];
|
|
pQp+=p[t]*Qp[t];
|
|
}
|
|
double max_error=0;
|
|
for (t=0;t<k;t++)
|
|
{
|
|
double error=fabs(Qp[t]-pQp);
|
|
if (error>max_error)
|
|
max_error=error;
|
|
}
|
|
if (max_error<eps) break;
|
|
|
|
for (t=0;t<k;t++)
|
|
{
|
|
double diff=(-Qp[t]+pQp)/Q[t][t];
|
|
p[t]+=diff;
|
|
pQp=(pQp+diff*(diff*Q[t][t]+2*Qp[t]))/(1+diff)/(1+diff);
|
|
for (j=0;j<k;j++)
|
|
{
|
|
Qp[j]=(Qp[j]+diff*Q[t][j])/(1+diff);
|
|
p[j]/=(1+diff);
|
|
}
|
|
}
|
|
}
|
|
if (iter>=max_iter)
|
|
info("Exceeds max_iter in multiclass_prob\n");
|
|
for(t=0;t<k;t++) free(Q[t]);
|
|
free(Q);
|
|
free(Qp);
|
|
}
|
|
|
|
// Cross-validation decision values for probability estimates
|
|
static void svm_binary_svc_probability(
|
|
const svm_problem *prob, const svm_parameter *param,
|
|
double Cp, double Cn, double& probA, double& probB)
|
|
{
|
|
int i;
|
|
int nr_fold = 5;
|
|
int *perm = Malloc(int,prob->l);
|
|
double *dec_values = Malloc(double,prob->l);
|
|
|
|
// random shuffle
|
|
for(i=0;i<prob->l;i++) perm[i]=i;
|
|
for(i=0;i<prob->l;i++)
|
|
{
|
|
int j = i+rand()%(prob->l-i);
|
|
swap(perm[i],perm[j]);
|
|
}
|
|
for(i=0;i<nr_fold;i++)
|
|
{
|
|
int begin = i*prob->l/nr_fold;
|
|
int end = (i+1)*prob->l/nr_fold;
|
|
int j,k;
|
|
struct svm_problem subprob;
|
|
|
|
subprob.l = prob->l-(end-begin);
|
|
subprob.x = Malloc(struct svm_node*,subprob.l);
|
|
subprob.y = Malloc(double,subprob.l);
|
|
subprob.W = Malloc(double,subprob.l);
|
|
|
|
k=0;
|
|
for(j=0;j<begin;j++)
|
|
{
|
|
subprob.x[k] = prob->x[perm[j]];
|
|
subprob.y[k] = prob->y[perm[j]];
|
|
subprob.W[k] = prob->W[perm[j]];
|
|
++k;
|
|
}
|
|
for(j=end;j<prob->l;j++)
|
|
{
|
|
subprob.x[k] = prob->x[perm[j]];
|
|
subprob.y[k] = prob->y[perm[j]];
|
|
subprob.W[k] = prob->W[perm[j]];
|
|
++k;
|
|
}
|
|
int p_count=0,n_count=0;
|
|
for(j=0;j<k;j++)
|
|
if(subprob.y[j]>0)
|
|
p_count++;
|
|
else
|
|
n_count++;
|
|
|
|
if(p_count==0 && n_count==0)
|
|
for(j=begin;j<end;j++)
|
|
dec_values[perm[j]] = 0;
|
|
else if(p_count > 0 && n_count == 0)
|
|
for(j=begin;j<end;j++)
|
|
dec_values[perm[j]] = 1;
|
|
else if(p_count == 0 && n_count > 0)
|
|
for(j=begin;j<end;j++)
|
|
dec_values[perm[j]] = -1;
|
|
else
|
|
{
|
|
svm_parameter subparam = *param;
|
|
subparam.probability=0;
|
|
subparam.C=1.0;
|
|
subparam.nr_weight=2;
|
|
subparam.weight_label = Malloc(int,2);
|
|
subparam.weight = Malloc(double,2);
|
|
subparam.weight_label[0]=+1;
|
|
subparam.weight_label[1]=-1;
|
|
subparam.weight[0]=Cp;
|
|
subparam.weight[1]=Cn;
|
|
struct svm_model *submodel = svm_train(&subprob,&subparam);
|
|
for(j=begin;j<end;j++)
|
|
{
|
|
svm_predict_values(submodel,prob->x[perm[j]],&(dec_values[perm[j]]));
|
|
// ensure +1 -1 order; reason not using CV subroutine
|
|
dec_values[perm[j]] *= submodel->label[0];
|
|
}
|
|
svm_free_and_destroy_model(&submodel);
|
|
svm_destroy_param(&subparam);
|
|
}
|
|
free(subprob.x);
|
|
free(subprob.y);
|
|
free(subprob.W);
|
|
}
|
|
sigmoid_train(prob->l,dec_values,prob->y,probA,probB);
|
|
free(dec_values);
|
|
free(perm);
|
|
}
|
|
|
|
// Return parameter of a Laplace distribution
|
|
static double svm_svr_probability(
|
|
const svm_problem *prob, const svm_parameter *param)
|
|
{
|
|
int i;
|
|
int nr_fold = 5;
|
|
double *ymv = Malloc(double,prob->l);
|
|
double mae = 0;
|
|
|
|
svm_parameter newparam = *param;
|
|
newparam.probability = 0;
|
|
svm_cross_validation(prob,&newparam,nr_fold,ymv);
|
|
for(i=0;i<prob->l;i++)
|
|
{
|
|
ymv[i]=prob->y[i]-ymv[i];
|
|
mae += fabs(ymv[i]);
|
|
}
|
|
mae /= prob->l;
|
|
double std=sqrt(2*mae*mae);
|
|
int count=0;
|
|
mae=0;
|
|
for(i=0;i<prob->l;i++)
|
|
if (fabs(ymv[i]) > 5*std)
|
|
count=count+1;
|
|
else
|
|
mae+=fabs(ymv[i]);
|
|
mae /= (prob->l-count);
|
|
info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g\n",mae);
|
|
free(ymv);
|
|
return mae;
|
|
}
|
|
|
|
|
|
// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
|
|
// perm, length l, must be allocated before calling this subroutine
|
|
static void svm_group_classes(const svm_problem *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm)
|
|
{
|
|
int l = prob->l;
|
|
int max_nr_class = 16;
|
|
int nr_class = 0;
|
|
int *label = Malloc(int,max_nr_class);
|
|
int *count = Malloc(int,max_nr_class);
|
|
int *data_label = Malloc(int,l);
|
|
int i;
|
|
|
|
for(i=0;i<l;i++)
|
|
{
|
|
int this_label = (int)prob->y[i];
|
|
int j;
|
|
for(j=0;j<nr_class;j++)
|
|
{
|
|
if(this_label == label[j])
|
|
{
|
|
++count[j];
|
|
break;
|
|
}
|
|
}
|
|
data_label[i] = j;
|
|
if(j == nr_class)
|
|
{
|
|
if(nr_class == max_nr_class)
|
|
{
|
|
max_nr_class *= 2;
|
|
label = (int *)realloc(label,max_nr_class*sizeof(int));
|
|
count = (int *)realloc(count,max_nr_class*sizeof(int));
|
|
}
|
|
label[nr_class] = this_label;
|
|
count[nr_class] = 1;
|
|
++nr_class;
|
|
}
|
|
}
|
|
|
|
int *start = Malloc(int,nr_class);
|
|
start[0] = 0;
|
|
for(i=1;i<nr_class;i++)
|
|
start[i] = start[i-1]+count[i-1];
|
|
for(i=0;i<l;i++)
|
|
{
|
|
perm[start[data_label[i]]] = i;
|
|
++start[data_label[i]];
|
|
}
|
|
start[0] = 0;
|
|
for(i=1;i<nr_class;i++)
|
|
start[i] = start[i-1]+count[i-1];
|
|
|
|
*nr_class_ret = nr_class;
|
|
*label_ret = label;
|
|
*start_ret = start;
|
|
*count_ret = count;
|
|
free(data_label);
|
|
}
|
|
|
|
//
|
|
// Remove zero weighed data as libsvm and some liblinear solvers require C > 0.
|
|
//
|
|
static void remove_zero_weight(svm_problem *newprob, const svm_problem *prob)
|
|
{
|
|
int i;
|
|
int l = 0;
|
|
for(i=0;i<prob->l;i++)
|
|
if(prob->W[i] > 0) l++;
|
|
*newprob = *prob;
|
|
newprob->l = l;
|
|
newprob->x = Malloc(svm_node*,l);
|
|
newprob->y = Malloc(double,l);
|
|
newprob->W = Malloc(double,l);
|
|
|
|
int j = 0;
|
|
for(i=0;i<prob->l;i++)
|
|
if(prob->W[i] > 0)
|
|
{
|
|
newprob->x[j] = prob->x[i];
|
|
newprob->y[j] = prob->y[i];
|
|
newprob->W[j] = prob->W[i];
|
|
j++;
|
|
}
|
|
}
|
|
|
|
//
|
|
// Interface functions
|
|
//
|
|
svm_model *svm_train(const svm_problem *prob, const svm_parameter *param)
|
|
{
|
|
svm_problem newprob;
|
|
remove_zero_weight(&newprob, prob);
|
|
prob = &newprob;
|
|
|
|
svm_model *model = Malloc(svm_model,1);
|
|
model->param = *param;
|
|
model->free_sv = 0; // XXX
|
|
|
|
if(param->svm_type == ONE_CLASS ||
|
|
param->svm_type == EPSILON_SVR ||
|
|
param->svm_type == NU_SVR)
|
|
{
|
|
// regression or one-class-svm
|
|
model->nr_class = 2;
|
|
model->label = NULL;
|
|
model->nSV = NULL;
|
|
model->probA = NULL; model->probB = NULL;
|
|
model->sv_coef = Malloc(double *,1);
|
|
|
|
if(param->probability &&
|
|
(param->svm_type == EPSILON_SVR ||
|
|
param->svm_type == NU_SVR))
|
|
{
|
|
model->probA = Malloc(double,1);
|
|
model->probA[0] = svm_svr_probability(prob,param);
|
|
}
|
|
|
|
decision_function f = svm_train_one(prob,param,0,0);
|
|
model->rho = Malloc(double,1);
|
|
model->rho[0] = f.rho;
|
|
|
|
int nSV = 0;
|
|
int i;
|
|
for(i=0;i<prob->l;i++)
|
|
if(fabs(f.alpha[i]) > 0) ++nSV;
|
|
model->l = nSV;
|
|
model->SV = Malloc(svm_node *,nSV);
|
|
model->sv_coef[0] = Malloc(double,nSV);
|
|
model->sv_indices = Malloc(int,nSV);
|
|
int j = 0;
|
|
for(i=0;i<prob->l;i++)
|
|
if(fabs(f.alpha[i]) > 0)
|
|
{
|
|
model->SV[j] = prob->x[i];
|
|
model->sv_coef[0][j] = f.alpha[i];
|
|
model->sv_indices[j] = i+1;
|
|
++j;
|
|
}
|
|
|
|
free(f.alpha);
|
|
}
|
|
else
|
|
{
|
|
// classification
|
|
int l = prob->l;
|
|
int nr_class;
|
|
int *label = NULL;
|
|
int *start = NULL;
|
|
int *count = NULL;
|
|
int *perm = Malloc(int,l);
|
|
|
|
// group training data of the same class
|
|
svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
|
|
if(nr_class == 1)
|
|
info("WARNING: training data in only one class. See README for details.\n");
|
|
|
|
svm_node **x = Malloc(svm_node *,l);
|
|
double *W;
|
|
W = Malloc(double,l);
|
|
|
|
int i;
|
|
for(i=0;i<l;i++)
|
|
{
|
|
x[i] = prob->x[perm[i]];
|
|
W[i] = prob->W[perm[i]];
|
|
}
|
|
|
|
// calculate weighted C
|
|
|
|
double *weighted_C = Malloc(double, nr_class);
|
|
for(i=0;i<nr_class;i++)
|
|
weighted_C[i] = param->C;
|
|
for(i=0;i<param->nr_weight;i++)
|
|
{
|
|
int j;
|
|
for(j=0;j<nr_class;j++)
|
|
if(param->weight_label[i] == label[j])
|
|
break;
|
|
if(j == nr_class)
|
|
fprintf(stderr,"WARNING: class label %d specified in weight is not found\n", param->weight_label[i]);
|
|
else
|
|
weighted_C[j] *= param->weight[i];
|
|
}
|
|
|
|
// train k*(k-1)/2 models
|
|
|
|
bool *nonzero = Malloc(bool,l);
|
|
for(i=0;i<l;i++)
|
|
nonzero[i] = false;
|
|
decision_function *f = Malloc(decision_function,nr_class*(nr_class-1)/2);
|
|
|
|
double *probA=NULL,*probB=NULL;
|
|
if (param->probability)
|
|
{
|
|
probA=Malloc(double,nr_class*(nr_class-1)/2);
|
|
probB=Malloc(double,nr_class*(nr_class-1)/2);
|
|
}
|
|
|
|
int p = 0;
|
|
for(i=0;i<nr_class;i++)
|
|
for(int j=i+1;j<nr_class;j++)
|
|
{
|
|
svm_problem sub_prob;
|
|
int si = start[i], sj = start[j];
|
|
int ci = count[i], cj = count[j];
|
|
sub_prob.l = ci+cj;
|
|
sub_prob.x = Malloc(svm_node *,sub_prob.l);
|
|
sub_prob.y = Malloc(double,sub_prob.l);
|
|
sub_prob.W = Malloc(double,sub_prob.l);
|
|
int k;
|
|
for(k=0;k<ci;k++)
|
|
{
|
|
sub_prob.x[k] = x[si+k];
|
|
sub_prob.y[k] = +1;
|
|
sub_prob.W[k] = W[si+k];
|
|
}
|
|
for(k=0;k<cj;k++)
|
|
{
|
|
sub_prob.x[ci+k] = x[sj+k];
|
|
sub_prob.y[ci+k] = -1;
|
|
sub_prob.W[ci+k] = W[sj+k];
|
|
}
|
|
|
|
if(param->probability)
|
|
svm_binary_svc_probability(&sub_prob,param,weighted_C[i],weighted_C[j],probA[p],probB[p]);
|
|
|
|
f[p] = svm_train_one(&sub_prob,param,weighted_C[i],weighted_C[j]);
|
|
for(k=0;k<ci;k++)
|
|
if(!nonzero[si+k] && fabs(f[p].alpha[k]) > 0)
|
|
nonzero[si+k] = true;
|
|
for(k=0;k<cj;k++)
|
|
if(!nonzero[sj+k] && fabs(f[p].alpha[ci+k]) > 0)
|
|
nonzero[sj+k] = true;
|
|
free(sub_prob.x);
|
|
free(sub_prob.y);
|
|
free(sub_prob.W);
|
|
++p;
|
|
}
|
|
|
|
// build output
|
|
|
|
model->nr_class = nr_class;
|
|
|
|
model->label = Malloc(int,nr_class);
|
|
for(i=0;i<nr_class;i++)
|
|
model->label[i] = label[i];
|
|
|
|
model->rho = Malloc(double,nr_class*(nr_class-1)/2);
|
|
for(i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
model->rho[i] = f[i].rho;
|
|
|
|
if(param->probability)
|
|
{
|
|
model->probA = Malloc(double,nr_class*(nr_class-1)/2);
|
|
model->probB = Malloc(double,nr_class*(nr_class-1)/2);
|
|
for(i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
{
|
|
model->probA[i] = probA[i];
|
|
model->probB[i] = probB[i];
|
|
}
|
|
}
|
|
else
|
|
{
|
|
model->probA=NULL;
|
|
model->probB=NULL;
|
|
}
|
|
|
|
int total_sv = 0;
|
|
int *nz_count = Malloc(int,nr_class);
|
|
model->nSV = Malloc(int,nr_class);
|
|
for(i=0;i<nr_class;i++)
|
|
{
|
|
int nSV = 0;
|
|
for(int j=0;j<count[i];j++)
|
|
if(nonzero[start[i]+j])
|
|
{
|
|
++nSV;
|
|
++total_sv;
|
|
}
|
|
model->nSV[i] = nSV;
|
|
nz_count[i] = nSV;
|
|
}
|
|
|
|
info("Total nSV = %d\n",total_sv);
|
|
|
|
model->l = total_sv;
|
|
model->SV = Malloc(svm_node *,total_sv);
|
|
model->sv_indices = Malloc(int,total_sv);
|
|
p = 0;
|
|
for(i=0;i<l;i++)
|
|
if(nonzero[i])
|
|
{
|
|
model->SV[p] = x[i];
|
|
model->sv_indices[p++] = perm[i] + 1;
|
|
}
|
|
|
|
int *nz_start = Malloc(int,nr_class);
|
|
nz_start[0] = 0;
|
|
for(i=1;i<nr_class;i++)
|
|
nz_start[i] = nz_start[i-1]+nz_count[i-1];
|
|
|
|
model->sv_coef = Malloc(double *,nr_class-1);
|
|
for(i=0;i<nr_class-1;i++)
|
|
model->sv_coef[i] = Malloc(double,total_sv);
|
|
|
|
p = 0;
|
|
for(i=0;i<nr_class;i++)
|
|
for(int j=i+1;j<nr_class;j++)
|
|
{
|
|
// classifier (i,j): coefficients with
|
|
// i are in sv_coef[j-1][nz_start[i]...],
|
|
// j are in sv_coef[i][nz_start[j]...]
|
|
|
|
int si = start[i];
|
|
int sj = start[j];
|
|
int ci = count[i];
|
|
int cj = count[j];
|
|
|
|
int q = nz_start[i];
|
|
int k;
|
|
for(k=0;k<ci;k++)
|
|
if(nonzero[si+k])
|
|
model->sv_coef[j-1][q++] = f[p].alpha[k];
|
|
q = nz_start[j];
|
|
for(k=0;k<cj;k++)
|
|
if(nonzero[sj+k])
|
|
model->sv_coef[i][q++] = f[p].alpha[ci+k];
|
|
++p;
|
|
}
|
|
|
|
free(label);
|
|
free(probA);
|
|
free(probB);
|
|
free(count);
|
|
free(perm);
|
|
free(start);
|
|
free(W);
|
|
free(x);
|
|
free(weighted_C);
|
|
free(nonzero);
|
|
for(i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
free(f[i].alpha);
|
|
free(f);
|
|
free(nz_count);
|
|
free(nz_start);
|
|
}
|
|
free(newprob.x);
|
|
free(newprob.y);
|
|
free(newprob.W);
|
|
return model;
|
|
}
|
|
|
|
// Stratified cross validation
|
|
void svm_cross_validation(const svm_problem *prob, const svm_parameter *param, int nr_fold, double *target)
|
|
{
|
|
int i;
|
|
int *fold_start = Malloc(int,nr_fold+1);
|
|
int l = prob->l;
|
|
int *perm = Malloc(int,l);
|
|
int nr_class;
|
|
|
|
// stratified cv may not give leave-one-out rate
|
|
// Each class to l folds -> some folds may have zero elements
|
|
if((param->svm_type == C_SVC ||
|
|
param->svm_type == NU_SVC) && nr_fold < l)
|
|
{
|
|
int *start = NULL;
|
|
int *label = NULL;
|
|
int *count = NULL;
|
|
svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
|
|
|
|
// random shuffle and then data grouped by fold using the array perm
|
|
int *fold_count = Malloc(int,nr_fold);
|
|
int c;
|
|
int *index = Malloc(int,l);
|
|
for(i=0;i<l;i++)
|
|
index[i]=perm[i];
|
|
for (c=0; c<nr_class; c++)
|
|
for(i=0;i<count[c];i++)
|
|
{
|
|
int j = i+rand()%(count[c]-i);
|
|
swap(index[start[c]+j],index[start[c]+i]);
|
|
}
|
|
for(i=0;i<nr_fold;i++)
|
|
{
|
|
fold_count[i] = 0;
|
|
for (c=0; c<nr_class;c++)
|
|
fold_count[i]+=(i+1)*count[c]/nr_fold-i*count[c]/nr_fold;
|
|
}
|
|
fold_start[0]=0;
|
|
for (i=1;i<=nr_fold;i++)
|
|
fold_start[i] = fold_start[i-1]+fold_count[i-1];
|
|
for (c=0; c<nr_class;c++)
|
|
for(i=0;i<nr_fold;i++)
|
|
{
|
|
int begin = start[c]+i*count[c]/nr_fold;
|
|
int end = start[c]+(i+1)*count[c]/nr_fold;
|
|
for(int j=begin;j<end;j++)
|
|
{
|
|
perm[fold_start[i]] = index[j];
|
|
fold_start[i]++;
|
|
}
|
|
}
|
|
fold_start[0]=0;
|
|
for (i=1;i<=nr_fold;i++)
|
|
fold_start[i] = fold_start[i-1]+fold_count[i-1];
|
|
free(start);
|
|
free(label);
|
|
free(count);
|
|
free(index);
|
|
free(fold_count);
|
|
}
|
|
else
|
|
{
|
|
for(i=0;i<l;i++) perm[i]=i;
|
|
for(i=0;i<l;i++)
|
|
{
|
|
int j = i+rand()%(l-i);
|
|
swap(perm[i],perm[j]);
|
|
}
|
|
for(i=0;i<=nr_fold;i++)
|
|
fold_start[i]=i*l/nr_fold;
|
|
}
|
|
|
|
for(i=0;i<nr_fold;i++)
|
|
{
|
|
int begin = fold_start[i];
|
|
int end = fold_start[i+1];
|
|
int j,k;
|
|
struct svm_problem subprob;
|
|
|
|
subprob.l = l-(end-begin);
|
|
subprob.x = Malloc(struct svm_node*,subprob.l);
|
|
subprob.y = Malloc(double,subprob.l);
|
|
|
|
subprob.W = Malloc(double,subprob.l);
|
|
k=0;
|
|
for(j=0;j<begin;j++)
|
|
{
|
|
subprob.x[k] = prob->x[perm[j]];
|
|
subprob.y[k] = prob->y[perm[j]];
|
|
subprob.W[k] = prob->W[perm[j]];
|
|
++k;
|
|
}
|
|
for(j=end;j<l;j++)
|
|
{
|
|
subprob.x[k] = prob->x[perm[j]];
|
|
subprob.y[k] = prob->y[perm[j]];
|
|
subprob.W[k] = prob->W[perm[j]];
|
|
++k;
|
|
}
|
|
struct svm_model *submodel = svm_train(&subprob,param);
|
|
if(param->probability &&
|
|
(param->svm_type == C_SVC || param->svm_type == NU_SVC))
|
|
{
|
|
double *prob_estimates=Malloc(double,svm_get_nr_class(submodel));
|
|
for(j=begin;j<end;j++)
|
|
target[perm[j]] = svm_predict_probability(submodel,prob->x[perm[j]],prob_estimates);
|
|
free(prob_estimates);
|
|
}
|
|
else
|
|
for(j=begin;j<end;j++)
|
|
target[perm[j]] = svm_predict(submodel,prob->x[perm[j]]);
|
|
svm_free_and_destroy_model(&submodel);
|
|
free(subprob.x);
|
|
free(subprob.y);
|
|
free(subprob.W);
|
|
}
|
|
free(fold_start);
|
|
free(perm);
|
|
}
|
|
|
|
|
|
int svm_get_svm_type(const svm_model *model)
|
|
{
|
|
return model->param.svm_type;
|
|
}
|
|
|
|
int svm_get_nr_class(const svm_model *model)
|
|
{
|
|
return model->nr_class;
|
|
}
|
|
|
|
void svm_get_labels(const svm_model *model, int* label)
|
|
{
|
|
if (model->label != NULL)
|
|
for(int i=0;i<model->nr_class;i++)
|
|
label[i] = model->label[i];
|
|
}
|
|
|
|
void svm_get_sv_indices(const svm_model *model, int* indices)
|
|
{
|
|
if (model->sv_indices != NULL)
|
|
for(int i=0;i<model->l;i++)
|
|
indices[i] = model->sv_indices[i];
|
|
}
|
|
|
|
int svm_get_nr_sv(const svm_model *model)
|
|
{
|
|
return model->l;
|
|
}
|
|
|
|
double svm_get_svr_probability(const svm_model *model)
|
|
{
|
|
if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&
|
|
model->probA!=NULL)
|
|
return model->probA[0];
|
|
else
|
|
{
|
|
fprintf(stderr,"Model doesn't contain information for SVR probability inference\n");
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
double svm_hyper_w_normsqr_twoclass(const struct svm_model* model)
|
|
{
|
|
assert(model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC);
|
|
int i, j;
|
|
// int nr_class = model->nr_class;
|
|
// assert(nr_class == 2);
|
|
int l = model->l;
|
|
|
|
double sum = 0;
|
|
double *coef = model->sv_coef[0];
|
|
|
|
for(i=0;i<l;++i)
|
|
for(j=0;j<l;++j)
|
|
{
|
|
double kv = Kernel::k_function(model->SV[i],model->SV[j],model->param);
|
|
sum += kv * coef[i] * coef[j];
|
|
}
|
|
|
|
return sum;
|
|
}
|
|
|
|
double svm_predict_values_twoclass(const struct svm_model* model, const struct svm_node* x)
|
|
{
|
|
|
|
assert(model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC);
|
|
int i;
|
|
// int nr_class = model->nr_class;
|
|
// assert(nr_class == 2);
|
|
int l = model->l;
|
|
|
|
double *kvalue = Malloc(double,l);
|
|
for(i=0;i<l;i++)
|
|
kvalue[i] = Kernel::k_function(x,model->SV[i],model->param);
|
|
|
|
|
|
double sum = 0;
|
|
double *coef = model->sv_coef[0];
|
|
for(i=0;i<l;++i)
|
|
sum += coef[i] * kvalue[i];
|
|
sum -= model->rho[0];
|
|
|
|
free(kvalue);
|
|
|
|
return sum * model->label[0];
|
|
}
|
|
|
|
double svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values)
|
|
{
|
|
int i;
|
|
if(model->param.svm_type == ONE_CLASS ||
|
|
model->param.svm_type == EPSILON_SVR ||
|
|
model->param.svm_type == NU_SVR)
|
|
{
|
|
double *sv_coef = model->sv_coef[0];
|
|
double sum = 0;
|
|
for(i=0;i<model->l;i++)
|
|
sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param);
|
|
sum -= model->rho[0];
|
|
*dec_values = sum;
|
|
|
|
if(model->param.svm_type == ONE_CLASS)
|
|
return (sum>0)?1:-1;
|
|
else
|
|
return sum;
|
|
}
|
|
else
|
|
{
|
|
int nr_class = model->nr_class;
|
|
int l = model->l;
|
|
|
|
double *kvalue = Malloc(double,l);
|
|
for(i=0;i<l;i++)
|
|
kvalue[i] = Kernel::k_function(x,model->SV[i],model->param);
|
|
|
|
int *start = Malloc(int,nr_class);
|
|
start[0] = 0;
|
|
for(i=1;i<nr_class;i++)
|
|
start[i] = start[i-1]+model->nSV[i-1];
|
|
|
|
int *vote = Malloc(int,nr_class);
|
|
for(i=0;i<nr_class;i++)
|
|
vote[i] = 0;
|
|
|
|
int p=0;
|
|
for(i=0;i<nr_class;i++)
|
|
for(int j=i+1;j<nr_class;j++)
|
|
{
|
|
double sum = 0;
|
|
int si = start[i];
|
|
int sj = start[j];
|
|
int ci = model->nSV[i];
|
|
int cj = model->nSV[j];
|
|
|
|
int k;
|
|
double *coef1 = model->sv_coef[j-1];
|
|
double *coef2 = model->sv_coef[i];
|
|
for(k=0;k<ci;k++)
|
|
sum += coef1[si+k] * kvalue[si+k];
|
|
for(k=0;k<cj;k++)
|
|
sum += coef2[sj+k] * kvalue[sj+k];
|
|
sum -= model->rho[p];
|
|
dec_values[p] = sum;
|
|
|
|
if(dec_values[p] > 0)
|
|
++vote[i];
|
|
else
|
|
++vote[j];
|
|
p++;
|
|
}
|
|
|
|
int vote_max_idx = 0;
|
|
for(i=1;i<nr_class;i++)
|
|
if(vote[i] > vote[vote_max_idx])
|
|
vote_max_idx = i;
|
|
|
|
free(kvalue);
|
|
free(start);
|
|
free(vote);
|
|
return model->label[vote_max_idx];
|
|
}
|
|
}
|
|
|
|
double svm_predict(const svm_model *model, const svm_node *x)
|
|
{
|
|
int nr_class = model->nr_class;
|
|
double *dec_values;
|
|
if(model->param.svm_type == ONE_CLASS ||
|
|
model->param.svm_type == EPSILON_SVR ||
|
|
model->param.svm_type == NU_SVR)
|
|
dec_values = Malloc(double, 1);
|
|
else
|
|
dec_values = Malloc(double, nr_class*(nr_class-1)/2);
|
|
double pred_result = svm_predict_values(model, x, dec_values);
|
|
free(dec_values);
|
|
return pred_result;
|
|
}
|
|
|
|
double svm_predict_probability(
|
|
const svm_model *model, const svm_node *x, double *prob_estimates)
|
|
{
|
|
if ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&
|
|
model->probA!=NULL && model->probB!=NULL)
|
|
{
|
|
int i;
|
|
int nr_class = model->nr_class;
|
|
double *dec_values = Malloc(double, nr_class*(nr_class-1)/2);
|
|
svm_predict_values(model, x, dec_values);
|
|
|
|
double min_prob=1e-7;
|
|
double **pairwise_prob=Malloc(double *,nr_class);
|
|
for(i=0;i<nr_class;i++)
|
|
pairwise_prob[i]=Malloc(double,nr_class);
|
|
int k=0;
|
|
for(i=0;i<nr_class;i++)
|
|
for(int j=i+1;j<nr_class;j++)
|
|
{
|
|
pairwise_prob[i][j]=min(max(sigmoid_predict(dec_values[k],model->probA[k],model->probB[k]),min_prob),1-min_prob);
|
|
pairwise_prob[j][i]=1-pairwise_prob[i][j];
|
|
k++;
|
|
}
|
|
multiclass_probability(nr_class,pairwise_prob,prob_estimates);
|
|
|
|
int prob_max_idx = 0;
|
|
for(i=1;i<nr_class;i++)
|
|
if(prob_estimates[i] > prob_estimates[prob_max_idx])
|
|
prob_max_idx = i;
|
|
for(i=0;i<nr_class;i++)
|
|
free(pairwise_prob[i]);
|
|
free(dec_values);
|
|
free(pairwise_prob);
|
|
return model->label[prob_max_idx];
|
|
}
|
|
else
|
|
return svm_predict(model, x);
|
|
}
|
|
|
|
static const char *svm_type_table[] =
|
|
{
|
|
"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL
|
|
};
|
|
|
|
static const char *kernel_type_table[]=
|
|
{
|
|
"linear","polynomial","rbf","sigmoid","precomputed",NULL
|
|
};
|
|
|
|
int svm_save_model(const char *model_file_name, const svm_model *model)
|
|
{
|
|
FILE *fp = fopen(model_file_name,"w");
|
|
if(fp==NULL) return -1;
|
|
|
|
char *old_locale = strdup(setlocale(LC_ALL, NULL));
|
|
setlocale(LC_ALL, "C");
|
|
|
|
const svm_parameter& param = model->param;
|
|
|
|
fprintf(fp,"svm_type %s\n", svm_type_table[param.svm_type]);
|
|
fprintf(fp,"kernel_type %s\n", kernel_type_table[param.kernel_type]);
|
|
|
|
if(param.kernel_type == POLY)
|
|
fprintf(fp,"degree %d\n", param.degree);
|
|
|
|
if(param.kernel_type == POLY || param.kernel_type == RBF || param.kernel_type == SIGMOID)
|
|
fprintf(fp,"gamma %g\n", param.gamma);
|
|
|
|
if(param.kernel_type == POLY || param.kernel_type == SIGMOID)
|
|
fprintf(fp,"coef0 %g\n", param.coef0);
|
|
|
|
int nr_class = model->nr_class;
|
|
int l = model->l;
|
|
fprintf(fp, "nr_class %d\n", nr_class);
|
|
fprintf(fp, "total_sv %d\n",l);
|
|
|
|
{
|
|
fprintf(fp, "rho");
|
|
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
fprintf(fp," %g",model->rho[i]);
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
if(model->label)
|
|
{
|
|
fprintf(fp, "label");
|
|
for(int i=0;i<nr_class;i++)
|
|
fprintf(fp," %d",model->label[i]);
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
if(model->probA) // regression has probA only
|
|
{
|
|
fprintf(fp, "probA");
|
|
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
fprintf(fp," %g",model->probA[i]);
|
|
fprintf(fp, "\n");
|
|
}
|
|
if(model->probB)
|
|
{
|
|
fprintf(fp, "probB");
|
|
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
|
|
fprintf(fp," %g",model->probB[i]);
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
if(model->nSV)
|
|
{
|
|
fprintf(fp, "nr_sv");
|
|
for(int i=0;i<nr_class;i++)
|
|
fprintf(fp," %d",model->nSV[i]);
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
fprintf(fp, "SV\n");
|
|
const double * const *sv_coef = model->sv_coef;
|
|
const svm_node * const *SV = model->SV;
|
|
|
|
for(int i=0;i<l;i++)
|
|
{
|
|
for(int j=0;j<nr_class-1;j++)
|
|
fprintf(fp, "%.16g ",sv_coef[j][i]);
|
|
|
|
const svm_node *p = SV[i];
|
|
|
|
if(param.kernel_type == PRECOMPUTED)
|
|
fprintf(fp,"0:%d ",(int)(p->value));
|
|
else
|
|
while(p->index != -1)
|
|
{
|
|
fprintf(fp,"%d:%.8g ",p->index,p->value);
|
|
p++;
|
|
}
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
setlocale(LC_ALL, old_locale);
|
|
free(old_locale);
|
|
|
|
if (ferror(fp) != 0 || fclose(fp) != 0) return -1;
|
|
else return 0;
|
|
}
|
|
|
|
static char *line = NULL;
|
|
static int max_line_len;
|
|
|
|
static char* readline(FILE *input)
|
|
{
|
|
int len;
|
|
|
|
if(fgets(line,max_line_len,input) == NULL)
|
|
return NULL;
|
|
|
|
while(strrchr(line,'\n') == NULL)
|
|
{
|
|
max_line_len *= 2;
|
|
line = (char *) realloc(line,max_line_len);
|
|
len = (int) strlen(line);
|
|
if(fgets(line+len,max_line_len-len,input) == NULL)
|
|
break;
|
|
}
|
|
return line;
|
|
}
|
|
|
|
svm_model *svm_load_model(const char *model_file_name)
|
|
{
|
|
FILE *fp = fopen(model_file_name,"rb");
|
|
if(fp==NULL) return NULL;
|
|
|
|
char *old_locale = strdup(setlocale(LC_ALL, NULL));
|
|
setlocale(LC_ALL, "C");
|
|
|
|
// read parameters
|
|
|
|
svm_model *model = Malloc(svm_model,1);
|
|
svm_parameter& param = model->param;
|
|
model->rho = NULL;
|
|
model->probA = NULL;
|
|
model->probB = NULL;
|
|
model->label = NULL;
|
|
model->nSV = NULL;
|
|
|
|
char cmd[81];
|
|
while(1)
|
|
{
|
|
fscanf(fp,"%80s",cmd);
|
|
|
|
if(strcmp(cmd,"svm_type")==0)
|
|
{
|
|
fscanf(fp,"%80s",cmd);
|
|
int i;
|
|
for(i=0;svm_type_table[i];i++)
|
|
{
|
|
if(strcmp(svm_type_table[i],cmd)==0)
|
|
{
|
|
param.svm_type=i;
|
|
break;
|
|
}
|
|
}
|
|
if(svm_type_table[i] == NULL)
|
|
{
|
|
fprintf(stderr,"unknown svm type.\n");
|
|
|
|
setlocale(LC_ALL, old_locale);
|
|
free(old_locale);
|
|
free(model->rho);
|
|
free(model->label);
|
|
free(model->nSV);
|
|
free(model);
|
|
return NULL;
|
|
}
|
|
}
|
|
else if(strcmp(cmd,"kernel_type")==0)
|
|
{
|
|
fscanf(fp,"%80s",cmd);
|
|
int i;
|
|
for(i=0;kernel_type_table[i];i++)
|
|
{
|
|
if(strcmp(kernel_type_table[i],cmd)==0)
|
|
{
|
|
param.kernel_type=i;
|
|
break;
|
|
}
|
|
}
|
|
if(kernel_type_table[i] == NULL)
|
|
{
|
|
fprintf(stderr,"unknown kernel function.\n");
|
|
|
|
setlocale(LC_ALL, old_locale);
|
|
free(old_locale);
|
|
free(model->rho);
|
|
free(model->label);
|
|
free(model->nSV);
|
|
free(model);
|
|
return NULL;
|
|
}
|
|
}
|
|
else if(strcmp(cmd,"degree")==0)
|
|
fscanf(fp,"%d",¶m.degree);
|
|
else if(strcmp(cmd,"gamma")==0)
|
|
fscanf(fp,"%lf",¶m.gamma);
|
|
else if(strcmp(cmd,"coef0")==0)
|
|
fscanf(fp,"%lf",¶m.coef0);
|
|
else if(strcmp(cmd,"nr_class")==0)
|
|
fscanf(fp,"%d",&model->nr_class);
|
|
else if(strcmp(cmd,"total_sv")==0)
|
|
fscanf(fp,"%d",&model->l);
|
|
else if(strcmp(cmd,"rho")==0)
|
|
{
|
|
int n = model->nr_class * (model->nr_class-1)/2;
|
|
model->rho = Malloc(double,n);
|
|
for(int i=0;i<n;i++)
|
|
fscanf(fp,"%lf",&model->rho[i]);
|
|
}
|
|
else if(strcmp(cmd,"label")==0)
|
|
{
|
|
int n = model->nr_class;
|
|
model->label = Malloc(int,n);
|
|
for(int i=0;i<n;i++)
|
|
fscanf(fp,"%d",&model->label[i]);
|
|
}
|
|
else if(strcmp(cmd,"probA")==0)
|
|
{
|
|
int n = model->nr_class * (model->nr_class-1)/2;
|
|
model->probA = Malloc(double,n);
|
|
for(int i=0;i<n;i++)
|
|
fscanf(fp,"%lf",&model->probA[i]);
|
|
}
|
|
else if(strcmp(cmd,"probB")==0)
|
|
{
|
|
int n = model->nr_class * (model->nr_class-1)/2;
|
|
model->probB = Malloc(double,n);
|
|
for(int i=0;i<n;i++)
|
|
fscanf(fp,"%lf",&model->probB[i]);
|
|
}
|
|
else if(strcmp(cmd,"nr_sv")==0)
|
|
{
|
|
int n = model->nr_class;
|
|
model->nSV = Malloc(int,n);
|
|
for(int i=0;i<n;i++)
|
|
fscanf(fp,"%d",&model->nSV[i]);
|
|
}
|
|
else if(strcmp(cmd,"SV")==0)
|
|
{
|
|
while(1)
|
|
{
|
|
int c = getc(fp);
|
|
if(c==EOF || c=='\n') break;
|
|
}
|
|
break;
|
|
}
|
|
else
|
|
{
|
|
fprintf(stderr,"unknown text in model file: [%s]\n",cmd);
|
|
|
|
setlocale(LC_ALL, old_locale);
|
|
free(old_locale);
|
|
free(model->rho);
|
|
free(model->label);
|
|
free(model->nSV);
|
|
free(model);
|
|
return NULL;
|
|
}
|
|
}
|
|
|
|
// read sv_coef and SV
|
|
|
|
int elements = 0;
|
|
long pos = ftell(fp);
|
|
|
|
max_line_len = 1024;
|
|
line = Malloc(char,max_line_len);
|
|
char *p,*endptr,*idx,*val;
|
|
|
|
while(readline(fp)!=NULL)
|
|
{
|
|
p = strtok(line,":");
|
|
while(1)
|
|
{
|
|
p = strtok(NULL,":");
|
|
if(p == NULL)
|
|
break;
|
|
++elements;
|
|
}
|
|
}
|
|
elements += model->l;
|
|
|
|
fseek(fp,pos,SEEK_SET);
|
|
|
|
int m = model->nr_class - 1;
|
|
int l = model->l;
|
|
model->sv_coef = Malloc(double *,m);
|
|
int i;
|
|
for(i=0;i<m;i++)
|
|
model->sv_coef[i] = Malloc(double,l);
|
|
model->SV = Malloc(svm_node*,l);
|
|
svm_node *x_space = NULL;
|
|
if(l>0) x_space = Malloc(svm_node,elements);
|
|
|
|
int j=0;
|
|
for(i=0;i<l;i++)
|
|
{
|
|
readline(fp);
|
|
model->SV[i] = &x_space[j];
|
|
|
|
p = strtok(line, " \t");
|
|
model->sv_coef[0][i] = strtod(p,&endptr);
|
|
for(int k=1;k<m;k++)
|
|
{
|
|
p = strtok(NULL, " \t");
|
|
model->sv_coef[k][i] = strtod(p,&endptr);
|
|
}
|
|
|
|
while(1)
|
|
{
|
|
idx = strtok(NULL, ":");
|
|
val = strtok(NULL, " \t");
|
|
|
|
if(val == NULL)
|
|
break;
|
|
x_space[j].index = (int) strtol(idx,&endptr,10);
|
|
x_space[j].value = strtod(val,&endptr);
|
|
|
|
++j;
|
|
}
|
|
x_space[j++].index = -1;
|
|
}
|
|
free(line);
|
|
|
|
setlocale(LC_ALL, old_locale);
|
|
free(old_locale);
|
|
|
|
if (ferror(fp) != 0 || fclose(fp) != 0)
|
|
return NULL;
|
|
|
|
model->free_sv = 1; // XXX
|
|
return model;
|
|
}
|
|
|
|
void svm_free_model_content(svm_model* model_ptr)
|
|
{
|
|
if(model_ptr->free_sv && model_ptr->l > 0 && model_ptr->SV != NULL)
|
|
free((void *)(model_ptr->SV[0]));
|
|
if(model_ptr->sv_coef)
|
|
{
|
|
for(int i=0;i<model_ptr->nr_class-1;i++)
|
|
free(model_ptr->sv_coef[i]);
|
|
}
|
|
|
|
free(model_ptr->SV);
|
|
model_ptr->SV = NULL;
|
|
|
|
free(model_ptr->sv_coef);
|
|
model_ptr->sv_coef = NULL;
|
|
|
|
free(model_ptr->rho);
|
|
model_ptr->rho = NULL;
|
|
|
|
free(model_ptr->label);
|
|
model_ptr->label= NULL;
|
|
|
|
free(model_ptr->probA);
|
|
model_ptr->probA = NULL;
|
|
|
|
free(model_ptr->probB);
|
|
model_ptr->probB= NULL;
|
|
|
|
free(model_ptr->nSV);
|
|
model_ptr->nSV = NULL;
|
|
}
|
|
|
|
void svm_free_and_destroy_model(svm_model** model_ptr_ptr)
|
|
{
|
|
if(model_ptr_ptr != NULL && *model_ptr_ptr != NULL)
|
|
{
|
|
svm_free_model_content(*model_ptr_ptr);
|
|
free(*model_ptr_ptr);
|
|
*model_ptr_ptr = NULL;
|
|
}
|
|
}
|
|
|
|
void svm_destroy_param(svm_parameter* param)
|
|
{
|
|
free(param->weight_label);
|
|
free(param->weight);
|
|
}
|
|
|
|
const char *svm_check_parameter(const svm_problem *prob, const svm_parameter *param)
|
|
{
|
|
// svm_type
|
|
|
|
int svm_type = param->svm_type;
|
|
if(svm_type != C_SVC &&
|
|
svm_type != NU_SVC &&
|
|
svm_type != ONE_CLASS &&
|
|
svm_type != EPSILON_SVR &&
|
|
svm_type != NU_SVR)
|
|
return "unknown svm type";
|
|
|
|
// kernel_type, degree
|
|
|
|
int kernel_type = param->kernel_type;
|
|
if(kernel_type != LINEAR &&
|
|
kernel_type != POLY &&
|
|
kernel_type != RBF &&
|
|
kernel_type != SIGMOID &&
|
|
kernel_type != PRECOMPUTED)
|
|
return "unknown kernel type";
|
|
|
|
if(param->gamma < 0)
|
|
return "gamma < 0";
|
|
|
|
if(param->degree < 0)
|
|
return "degree of polynomial kernel < 0";
|
|
|
|
// cache_size,eps,C,nu,p,shrinking
|
|
|
|
if(param->cache_size <= 0)
|
|
return "cache_size <= 0";
|
|
|
|
if(param->eps <= 0)
|
|
return "eps <= 0";
|
|
|
|
if(svm_type == C_SVC ||
|
|
svm_type == EPSILON_SVR ||
|
|
svm_type == NU_SVR)
|
|
if(param->C <= 0)
|
|
return "C <= 0";
|
|
|
|
if(svm_type == NU_SVC ||
|
|
svm_type == ONE_CLASS ||
|
|
svm_type == NU_SVR)
|
|
if(param->nu <= 0 || param->nu > 1)
|
|
return "nu <= 0 or nu > 1";
|
|
|
|
if(svm_type == EPSILON_SVR)
|
|
if(param->p < 0)
|
|
return "p < 0";
|
|
|
|
if(param->shrinking != 0 &&
|
|
param->shrinking != 1)
|
|
return "shrinking != 0 and shrinking != 1";
|
|
|
|
if(param->probability != 0 &&
|
|
param->probability != 1)
|
|
return "probability != 0 and probability != 1";
|
|
|
|
if(param->probability == 1 &&
|
|
svm_type == ONE_CLASS)
|
|
return "one-class SVM probability output not supported yet";
|
|
|
|
|
|
// check whether nu-svc is feasible
|
|
|
|
if(svm_type == NU_SVC)
|
|
{
|
|
int l = prob->l;
|
|
int max_nr_class = 16;
|
|
int nr_class = 0;
|
|
int *label = Malloc(int,max_nr_class);
|
|
double *count = Malloc(double,max_nr_class);
|
|
|
|
int i;
|
|
for(i=0;i<l;i++)
|
|
{
|
|
int this_label = (int)prob->y[i];
|
|
int j;
|
|
for(j=0;j<nr_class;j++)
|
|
if(this_label == label[j])
|
|
{
|
|
count[j] += prob->W[i];
|
|
break;
|
|
}
|
|
if(j == nr_class)
|
|
{
|
|
if(nr_class == max_nr_class)
|
|
{
|
|
max_nr_class *= 2;
|
|
label = (int *)realloc(label,max_nr_class*sizeof(int));
|
|
count = (double *)realloc(count,max_nr_class*sizeof(double));
|
|
}
|
|
label[nr_class] = this_label;
|
|
count[nr_class] = prob->W[i];
|
|
++nr_class;
|
|
}
|
|
}
|
|
|
|
for(i=0;i<nr_class;i++)
|
|
{
|
|
double n1 = count[i];
|
|
for(int j=i+1;j<nr_class;j++)
|
|
{
|
|
double n2 = count[j];
|
|
if(param->nu*(n1+n2)/2 > min(n1,n2))
|
|
{
|
|
free(label);
|
|
free(count);
|
|
return "specified nu is infeasible";
|
|
}
|
|
}
|
|
}
|
|
free(label);
|
|
free(count);
|
|
}
|
|
|
|
return NULL;
|
|
}
|
|
|
|
int svm_check_probability_model(const svm_model *model)
|
|
{
|
|
return ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) &&
|
|
model->probA!=NULL && model->probB!=NULL) ||
|
|
((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&
|
|
model->probA!=NULL);
|
|
}
|
|
|
|
void svm_set_print_string_function(void (*print_func)(const char *))
|
|
{
|
|
if(print_func == NULL)
|
|
svm_print_string = &print_string_stdout;
|
|
else
|
|
svm_print_string = print_func;
|
|
}
|